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Due to the coupling of multiple physical fields inside proton exchange membrane fuel cell (PEMFC) and the complex and changeable external application scenarios, the faults of PEMFC system greatly affects its engineering application. The paper proposes an ensemble learning-based fault diagnosis method for PEMFC systems, utilizing five distinct algorithms to develop an integrated ensemble classifier for fault detection. The original sample dataset collected from fuel cells undergoes a process of deep feature extraction to obtain a composite feature sample dataset, which is then inputted into an ensemble classifier to establish a fault diagnosis model. The experimental data of four kinds of faults of 80 W fuel cell test bench, EC fuel cell system and 50 W water-cooled fuel cell test bench are analyzed by using this classification model. The results show that the accuracy and generalization of the ensemble learning algorithm for different fault diagnosis of PEMFC system in different application environments are verified. • Adopt multiple classification algorithms for fault modeling and diagnosis. • Good applicability to different faults is shown in different test scenarios. • Compared with a single algorithm, it has higher fault identification accuracy. [ABSTRACT FROM AUTHOR]

Diagnosis of incipient faults of metro train bearings is a difficult problem under the double masking of strong wheel-rail impact interference and background noise. A novel feature extraction method using improved complementary complete local mean decomposition with adaptive noise (ICCELMDAN) and mixture correntropy-based adaptive feature enhancement (AFE) is proposed in this paper. The ICCELMDAN method uses a proposed complementary adaptive noise-assisted iterative sifting method to improve its anti-mixing and anti-splitting performance, and then can extract the complete feature from faulty bearing signals under strong background noise. The AFE method adaptively obtains the optimal parameters of mixture correntropy (MC) by employing a newly developed fault energy of mixture correntropy as the objective function in the marine predators algorithm (MPA), and can enhance the weak fault characteristic signal under strong wheel-rail impact interferences. The proposed method effectively combines the complete feature extraction capability of ICCELMDAN and the powerful feature enhancement capability of AFE, which can accurately diagnose the weak faults of metro train bearings under strong wheel-rail impact interferences in simulated and practical scenarios. Furthermore, it outperforms the existing methods in completeness of feature extraction, diagnosis accuracy and robustness from the comparative studies. • Present a complementary adaptive noise-assisted iterative sifting method. • Enhance the impulse interference suppress adaptability by a mixture correntropy based AFE method. • Extract complete feature of incipient bearing fault by a proposed ICCELMDAN method. • Diagnose the incipient faults of metro bearing under strong wheel-rail interference and background noise. [ABSTRACT FROM AUTHOR]

Accurate and timely fault diagnosis is a vital task to ensure process safety of modern industrial facilities. Motivated by the complex variations of process signals and mutual coupling of faults, this paper presents a multi-feature-based fault diagnosis method based on the weighted timeliness Broad Learning System (BLS). The proposed method fuses multiple features extracted from the original process data to improve the fault diagnosis performance, and makes the diagnosis model suitable for dynamic fault diagnosis problems by incorporating the BLS. The major contributions of this study are twofolds: 1) A systematic multi-feature extraction method is proposed to extract long-term trend features, short-term trend features, and binary alarm signals, which reflect the direction and amplitude changes of process signals under faulty conditions; 2) a weighted timeliness BLS structure with multiple fault-sensitive features as the input is proposed to ensure the dynamic characteristics of the fault diagnosis model. The designed fault diagnosis model can be updated in an incremental manner, and thus can improve the model updating efficiency while ensuring accuracy. The effectiveness and superiority of the proposed method is demonstrated by a case study based on the Tennessee Eastman benchmark process. [ABSTRACT FROM AUTHOR]

With the scale expansion of industrial processes, safety has become one of its important links and the requirements for safety monitoring are getting higher. How to realize timely and effective fault diagnosis, especially for incipient faults, has attracted more discussion and research. This paper proposes a novel pattern classification integrated global−local preserving projections (GLPP) and improved adaptive rank-order morphological filter (IAROMF) for fault diagnosis. First, in order to preserve the global manifold information and local manifold information of the data, GLPP is introduced to extract the features of the data to obtain the test signal and the template signal. Second, AROMF transformation is performed on the test signal and template signal to obtain the output trend feature. Third, as the pattern matching by Euclidean distance-based AROMF has the restriction of sequence timing and the feature points need to be strictly corresponding, the Weighted Dynamic Time Warping (WDTW) distance is used to calculate the total error of iteration between the template trend and the output trend. In order to prove the effectiveness of the method proposed in this paper, a case study was carried out on the Tennessee Eastman (TE) process. The experiment results illustrated that the novel pattern classification method proposed in this paper has higher diagnostic accuracy than other fault diagnosis methods, especially for incipient faults. [ABSTRACT FROM AUTHOR]

In recent years, supervised deep learning-based methods have achieved significant results in fuel cell system fault diagnosis. However, most existing deep learning-based fault prediction methods suffer from missing fault labels and data limitations due to the difficulty in obtaining fault and degradation data in real fuel cell systems. To address the above challenges, this paper proposes a fault diagnosis method based on digital twin and unsupervised domain adaptive learning. The method has two key features: First, a maximum-relevance minimum-redundancy algorithm is used to select the input signals. Then a high-order fuel cell system model is developed to obtain a large amount of digital domain fault data at low cost by simulating fault injection. Second, domain-invariant features are extracted using a domain-adaptive adversarial learning approach to reduce the distribution differences between the digital and real domains. The method successfully diagnosed nine typical faults in the fuel cell air, hydrogen, and thermal subsystems without real data fault labels. Under dynamic load conditions, the diagnostic accuracy reached 92.5 %. In addition, the method achieves a diagnostic accuracy of over 90 % under domain adversarial training using only normal real data. The experimental results show that the proposed method can achieve fuel cell system fault diagnosis without fault labels and significantly reduce the dependence on fault data. [Display omitted] • An unsupervised fault diagnosis method for PEMFC systems is proposed. • The diagnostic algorithm is investigated for nine typical faults. • The training process of diagnostic framework does not require fault labels. • The algorithm still has high accuracy using only normal test data for training. [ABSTRACT FROM AUTHOR]

This paper proposes an improved bubble entropy algorithm called Improved Hierarchical Refined Composite Multiscale Multichannel Bubble Entropy (IHRCMMCBE) to characterize the fault characteristics of rotating machinery. By introducing the refined composite multiscale analysis algorithm, the improved hierarchical decomposition algorithm, and the multi-channel data analysis method, the bubble entropy algorithm can more fully characterize the fault characteristics. Then, this method is combined with the machine learning algorithm to realize automatic fault diagnosis of rotating machinery. Multi-Cluster Feature Selection (MCFS) is used for feature selection to screen out useless features and improve feature extraction efficiency and feature quality. The MCFS method has excellent performance and does not need parameters, so it is suitable for adaptive fault diagnosis. Meanwhile, the EigenClass classifier using the concept of generalized eigenvalue is used to realize fault recognition. Besides, the recently proposed Gorilla Troops Optimizer (GTO) algorithm is used to improve EigenClass, and a GTO-optimized EigenClass (GTO-EigenClass) classifier is proposed to adaptively select parameters and achieve adaptive fault identification. The proposed fault diagnosis method of rotating machinery based on the improved bubble entropy is independent of data and application scenarios and is hardly affected by subjective factors. The ablation experiment proves the effectiveness of each improvement of bubble entropy. Also, the performance verification experiments and the comparative experiments prove that the method can effectively characterize different types of rotating machinery, different fault types, fault degrees, and composite faults, and it can achieve higher calculation efficiency and classification accuracy. When the sample length is 1024, the average accuracy of bearing and gearbox fault identification can reach 97.85 % and 97.57 % respectively, and when the sample length is 2048, the identification accuracy can reach 100 %. [ABSTRACT FROM AUTHOR]

This paper deals with the development of a novel deep learning framework to achieve highly accurate rotating machinery fault diagnosis using residual wide-kernel deep convolutional auto-encoder. Unlike most existing methods, in which the input data is processed by fast Fourier transform (FFT) and wavelet transform, this paper aims to learn important features from limited raw vibration signals. Firstly, the wide-kernel convolutional layer is introduced in the convolutional auto-encoder that can ensure the model can learn effective features from the data without any signal processing. Secondly, the residual learning block is introduced in convolutional auto-encoder that can ensure the model with sufficient depth without gradient vanishing and overfitting problems. Thirdly, convolutional auto-encoder can learn constructive features without massive data. To evaluate the performance of the proposed model, Case Western Reserve University (CWRU) bearing dataset and Southeast University (SEU) gearbox dataset are used to test. The experiment results and comparisons verify the denoising and feature extraction ability of the proposed model in the case of very few training samples. [ABSTRACT FROM AUTHOR]

Strong noise interference or compound fault coupling phenomenon may lead to the failure of fault diagnosis technology. This paper focuses on weak feature extraction and compound faults detection for rotating machinery fault diagnosis and proposes adaptive symplectic geometric mode decomposition (SGMD) using cycle kurtosis entropy. Firstly, an index named cycle kurtosis entropy (CKE) is presented to measure the strength of periodic impulses in a signal. The CKE uses the entropy value of calculating all delay cycle kurtosis (CK) to overcome the shortcomings of the CK in adaptive ability and obtain more stable values. Thirdly, CKE is applied to construct an adaptive slip window with optimal length. This process is called the adaptive window segmentation method, which is mainly used to dig out weak fault features in signals. Finally, CKE is used as the component selection criterion to select the components decomposed by SGMD. The selected components are reconstructed to obtain a de-noised signal. Hilbert envelope analysis is applied to the denoised signal to demodulate the fault characteristic frequency. Numerical simulations and experimental investigations using bearings and gears are performed to testify the property of the presented method. The results indicate that the adaptive slip window can enhance the decomposing ability of SGMD under strong noise condition. Moreover, for the strong periodic impulse identification ability, the cycle kurtosis entropy is suitable to determine the optimal components of SGMD. It is expected that the presented method will be effectively used for fault feature extractions in rotating machinery under stationary running conditions. [Display omitted] • An improved version of symplectic geometry mode decomposition is proposed for rotating machinery faults diagnosis. • The short time series can be obtained by adding a slip window. • Cycle kurtosis entropy is proposed to characterize the periodic impact. • Performance is validated by numerical and experimental investigations to detect faults in bearings and gears. [ABSTRACT FROM AUTHOR]

Automatic fault diagnosis in many systems is performed via the analysis of vibration data in the time or frequency domain. In the literature, many approaches for extracting featured from signals for fault diagnosis have been proposed. In this paper, we apply a variety of transform functions including Fourier, Wavelet, etc. to extract features from the vibration data, which includes the statistical features and some features automatically extracted via Convolutional Neural Networks (CNNs). For each of these feature extraction approaches, a learning algorithm is trained to diagnose the faults and their results are aggregated in an ensemble machine learning algorithm. The weights of the base learner algorithms are optimized via an evolutionary algorithm to achieve the best-weighted voting scheme. The architecture of CNNs has a significant effect on the performance of the algorithm, thus, in this paper, an evolutionary algorithm is proposed to find the best architecture for CNNs in fault diagnosis. The CNNs are trained via gradient descent algorithms which suffer from getting stuck in local optima. To manage this, we propose an evolutionary algorithm that benefits from the speed of gradient descent and the global search of evolutionary algorithms. The proposed algorithm is tested on a number of benchmark problems and the experimental results are presented. • An ensemble algorithm is proposed for fault diagnosis. • Different feature extractors are exploited. • An evolutionary training algorithm is proposed. [ABSTRACT FROM AUTHOR]

Fault diagnosis is an important technology for performing intelligent manufacturing. To simultaneously maintain high manufacturing quality and low failure rate for manufacturing systems, it is of great value to accurately locate the fault element, evaluate the fault severity and find the fault root cause. In order to effectively perform feature extraction for rotating machinery fault diagnosis, a robust discriminant latent variable manifold learning (RDLVML) algorithm is proposed in this paper. Specifically, RDLVML algorithm could effectively select the features of the high-dimensional fault data, and extract the low-dimensional fault features with better discrimination. Through the idea of discriminative learning, the accuracy of fault diagnosis was improved by increasing the aggregation of the same state feature samples and the difference between different classes of feature samples. A novel weighted neighborhood graph is proposed by constructing the q -Rényi kernel function, which could effectively suppress the interference of outliers and noise, and make the RDLVML algorithm more robust. Moreover, the fault diagnosis method of rotating machinery based on RDLVML is proposed in this paper. Through experimental verifications and comparisons with several classical feature selection algorithms, rotating machinery fault diagnosis could be more accurately performed by this method. [ABSTRACT FROM AUTHOR]

Deep learning has become the prevailing trend of intelligent fault diagnosis for rotating machines. Compared to early-stage methods, deep learning methods use automatic feature extraction instead of manual feature design. However, conventional intelligent diagnosis models are trapped by a dilemma that simple models are unable to tackle difficult cases, while complicated models are likely to over-parameterize. In this paper, a transformer-based model, Periodic Representations for Transformers (PRT) is proposed. PRT uses a dense-overlapping split strategy to enhance the feature learning inside sequence patches. Combined with the inherent capability of capturing long range dependencies of transformer, and the further information extraction of class-attention, PRT has excellent feature extraction abilities and could capture characteristic features directly from raw vibration signals. Moreover, PRT adopts a two-stage positional encoding method to encode position information both among and inside patches, which could adapt to different input lengths. A novel inference method to use larger inference sample sizes is further proposed to improve the performance of PRT. The effectiveness of PRT is verified on two datasets, where it achieves comparable and even better accuracies than the benchmark and state-of-the-art methods. PRT has the least FLOPs among the best performing models and could be further improved by the inference strategy, reaching an accuracy near 100%. • A PRT model with enhanced feature learning ability is introduced to fault diagnosis. • A self-adapted positional encoding method is designed for PRT. • A novel training and inference strategy to further improve PRT is proposed. [ABSTRACT FROM AUTHOR]

This paper proposes a selective kernel convolution deep residual network based on the channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis. First, adjacent channel attention modules are connected with the spatial attention mechanism module, then all channel features and spatial features are fused and a channel-spatial attention mechanism is constructed to form the feature enhancement module. Second, the feature enhancement module is embedded in a series model based on selective kernel convolution and deep residual network and combined with multi-layer feature fusion information. The model can more effectively extract fault features from the vibration signal, compared with traditional deep learning methods, and the fault recognition efficiency is improved. Finally, the proposed method was used to experimentally diagnose bearing and gear faults, and identification accuracies of 99.87% and 97.77%, respectively, were achieved. Compared with similar algorithms, the proposed method has higher fault identification ability, thereby demonstrating the advantages of the channel-spatial attention mechanism network. In addition, the accuracy and robustness of the model were verified. • The SKC deep residual network based on CSAM and feature fusion is proposed for mechanical fault diagnosis. • CSAM is used to enhance the ability of feature extraction. • The proposed method overcomes the feature visualization of deep learning models. • Results show high accuracy in diagnosing in bearing and gear. [ABSTRACT FROM AUTHOR]

Early fault diagnosis method under multiple operating conditions (OCs) is very important to improve the reliability of the permanent magnet synchronous motors (PMSMs) system. In this paper, a novel early fault diagnosis method for irreversible demagnetization fault and inter-turn short fault under multiple OCs in PMSM is proposed. Multi-layer self-attention mechanism is constructed to capture the long time dependency from the sensor signal, which can fuse the features of individual OCs to diagnose faults under unseen OCs. Furthermore, local feature extraction structure with end-to-end deep learning features and time–frequency domain features is proposed to extract effective features from early faults. The proposed method uses only two-phase current signals as the inputs to diagnose the faults. By comparing 1D convolutional neural network structure with multi-variables and universal machine learning methods, the experimental results show that the proposed method achieves high accuracy for early fault diagnosis of PMSM in unseen OCs. • An early fault diagnosis for PMSMs under multiple operating conditions is proposed. • A local feature extractor with high-order and statistical features is proposed. • Only two-phase currents of the permanent magnet synchronous motor are used as inputs. [ABSTRACT FROM AUTHOR]

• One new 1DCNN is established by adding a midpoint residual block before the middle layer. • A multiscale cascade structure is constructed to extract features from the original data. • The feature extraction capability is improved by multiscale cascade midpoint residual. • The anti-noise robustness is greatly improved by utilizing the ELU activation function. Convolutional Neural Network (CNN) has been widely used in mechanical fault diagnosis system, and has achieved satisfactory results. However, some limitations of the number of network layers and a single fixed convolution kernel are also exposed during performing the task of fault classification. To solve these problems, this paper proposes a multiscale cascade midpoint residual convolutional neural network (MSC-MpResCNN). Firstly, a new multiscale cascade structure is introduced to extract multi-resolution features contained in the original data. Secondly, the improved midpoint residual block is adopted in each branch of multiscale cascade structure to address deep network performance degradation. In addition, exponential linear unit (ELU) replaces the original linear rectification function, which makes the noise resistance of the model stronger and increasesthe robustness and generalization. L2 weight regularization and global average pooling (GAP) are applied to the model to avoid overfitting. The feasibility of the proposed method is validated by the experiments. The results indicates that the method can obtain higher fault recognition rate compared with previous methods by multiscale cascade midpoint residual block. Furthermore, the method has great anti-noise robustness under strong noise environment. [ABSTRACT FROM AUTHOR]

Fault diagnosis has a great significance in preventing serious failures of rotating machinery and avoiding huge economic losses. The performance of the existing fault diagnosis approaches might be affected by two factors, i.e., the quality of fault features extracted from monitoring signals and the capability of fault diagnosis model. This paper proposes a new fault diagnosis method combined mel-frequency cepstral coefficients (MFCC) with a designed parallel multi-fusion convolutional neural network (MFCNN) Specifically, a MFCC-based feature extraction method is defined to reduce the noise components in monitoring signal of rotating machinery and extract more useful low-frequency fault information for downstream task. Furthermore, a novel MFCNN is designed to enrich the high-level features after each convolution operation by using multiple activation functions, so as to improve the quality of the obtained fault features. Meanwhile, a new parallel MFCNN is constructed by using a defined structural ensemble operation to improve its diagnostic performance in different noise environments. Two typical bearing and gearbox failure datasets are applied to evaluate the performance of the proposed fault diagnosis method. The experimental results indicate that the proposed parallel MFCNN has the better diagnostic performance than other methods. • A MFCC-based feature extraction method is designed to obtain more useful fault features. • A novel MFCNN is created to enhance its feature extraction capability without increasing network parameters. • A parallel MFCNN is proposed to improve the diagnostic performance under different noise environments. [ABSTRACT FROM AUTHOR]

In practical engineering scenarios with limited labeled samples, conventional semi-supervised diagnostic methods face challenges in achieving satisfactory identification outcomes. To address the aforementioned issues, this paper introduces an incremental semi-supervised learning (ISL) approach based on boosting efficient attention (BEA) assisted cyclic adversarial auto-encoder (CAAE), referred to as BEA-CAAE. The CAAE enhances unsupervised feature representation by simultaneously constraining the distribution of encoded features and aligning elements of the reconstructed samples through a cyclic encoding strategy. The BEA improves classical attention weights' activation strength to better capture vital information, thereby boosting the feature extraction capabilities of both CAAE and the classifier. The ISL employs a stepwise pseudo-label propagation strategy to incrementally filter high-confidence samples, enhancing sample and label utilization, and improving diagnostic accuracy under low label rate conditions. Experiments conducted on multiple test rigs with simple structures as well as large-scale rotating components test rigs that mimic real-world engineering conditions have demonstrated that the proposed method exhibits a significant advantage over existing semi-supervised fault diagnosis approaches in terms of fault diagnosis accuracy and generalization capability, especially under low label rate conditions. [ABSTRACT FROM AUTHOR]

• The global and multi-scale local causal features are extracted to interpret the model and improve diagnosis accuracy. • The performance of the initial model can be improved by adopting an extensible framework to add enhancement nodes. • The sample incremental learning is developed to update incrementally based on the previous models without retraining. • Two experiments on bearing components validate the effectiveness and superiority of the proposed method. Intelligent fault diagnosis (IFD) plays a significant function in ensuring the reliable operation of mechanical equipment. However, most existing IFD methods are trained in batch learning way and the feature extraction process is unexplainable and it is challenging to satisfy the complex diagnosis requirements. Thus, this paper proposes a causal broad learning model (CBLM) guided by global and multi-scale local causal features for incremental machinery fault diagnosis. Firstly, rich global and multi-scale local causal features are extracted for training CBLM. Then, incremental learning capability is developed to update model by expanding or modifying the original weights, considerably reducing the time consumption and greatly improving the computational efficiency. When the initial model has inadequate nodes for feature learning, CBLM performs structural incremental learning by adding extra nodes to improve the diagnostic performance. As new samples with various fault degrees are entered, sample incremental learning is built to rapidly update based on previous model without retraining. Finally, two experimental results on bearings indicate that CBLM improves the testing accuracy of the initial model by 0.13% to 52.22% and 1.95% to 57.64%, respectively, and remains above 97.22% and 99.77% for the updated models. The training time consumption is reduced by 25.06 s to 133.0 s, 20.78 s and 141.9 s, respectively, and the subsequent models update time are only about 12 s and 10 s. Consequently, the proposed CBLM is an effective online IFD method because it is obviously superior to the existing IFD methods in regards to time consumption and diagnosis accuracy. [ABSTRACT FROM AUTHOR]

Synchronous motors play a significant role in a wide range of industrial applications. Their reliable operation is paramount. Any faults in synchronous motors can lead to costly downtime, decreased productivity, and potential safety hazards. By accurately diagnosing and classifying faults, we can proactively address issues before they escalate, ensuring the smooth operation of synchronous motors and minimizing the risk of equipment failure. The accurate diagnosis and fault detection in synchronous motors pose a significant challenge in their operation and maintenance. In the existing models, the feature data at various depths are not thoroughly extracted to maximize their feature extraction capability. Additionally, they employ a single support vector machine to make the final decision on the output. The single support vector machine may not consistently produce more accurate outcomes. Therefore, this paper proposes a novel fault diagnosis model based on a deep residual neural network and multiple support vector machines to diagnose mechanical and electrical faults of synchronous motors. The proposed model improves upon existing fault diagnosis models in two key aspects. Firstly, by employing a deep neural network, the model is able to effectively process and extract fault features from the motor fault dataset, capturing more nuanced information that may be missed by existing models. Secondly, the use of multiple support vector machines enhances the decision-making capability of the model, allowing for more accurate fault diagnosis. By combining these two aspects, the proposed model achieves superior diagnostic performance compared to single support vector machine-based models. Our proposed model has been rigorously evaluated using mechanical and electrical fault datasets, and the results of experimental tests clearly demonstrate its superior diagnostic performance when compared to existing fault diagnosis models. The synergy of deep neural networks and multiple support vector machines not only improves fault detection accuracy but also enhances the robustness and generalizability of the model, making it a valuable tool for real-world industrial applications. • A novel model is proposed for synchronous motor fault diagnostics. • A deep residual neural network effectively extracts fault features. • Multiple support vector machines enhance the detection capability. • Proposed model is compared with existing models. [ABSTRACT FROM AUTHOR]

Centrifugal pumps find extensive applications in various industries, being important to monitor their healthy operation. In this regard, the utilization of machine learning techniques for fault diagnosis, particularly in the domain of Intelligent Fault Diagnosis, assumes a pivotal role. This paper aims to contribute to this field by introducing an edge-based soft sensor approach for anomaly detection. The proposed strategy, grounded in machine learning principles, aims to diagnose the operational states of centrifugal pumps, distinguishing between healthy operation and various fault conditions. The study specifically investigates two types of faults: cavitation and dry running. The proposed Intelligent Fault Diagnosis only considers information available in industrial communication networks, which are usually provided at a high sampling rate, and underutilized by automation and supervisory systems, not requiring dedicated sensors. An experimental test bench was used and the centrifugal pump was operated under different conditions. Features were extracted, creating a dataset that represents system behavior. A Support Vector Machine-based model was applied for supervised data, and a One-Class Support Vector Machine-based model for unsupervised data, especially for users unable to operate the equipment under faulty conditions in the training phase. An easy feature selection strategy was employed to drastically reduce computational effort and improve the system performance, especially in the unsupervised data approach. The results achieve an accuracy of 100% for the supervised data approach and reach up to 96.4% for cavitation and 99.9% for dry running for the unsupervised data approach. [Display omitted] • A soft sensor methodology for Intelligent fault Detection for centrifugal pumps. • The use of supervised and unsupervised classification algorithms. • The proposal's performance is verified using a developed test bench. • An edge-based infrastructure for the proposed methodology. [ABSTRACT FROM AUTHOR]

Fault diagnosis of rotating machines is essential for the safe and efficient operation of maritime vessels. It prevents potential failures in rotating machines in maritime systems. Simultaneously developed faults severely damage the machines, leading to unprecedented incidents and higher maintenance costs. Data availability for developing multi-fault diagnosis solutions is limited, creating maintainability problems. The unavailability of data also causes further obstacles in the accurate diagnosis of machines. The efficiency of maritime machinery can be improved with intelligent diagnosis by advanced machine learning algorithms. This paper proposes a methodology to diagnose multi-faults with balanced and imbalanced acoustic, vibration and current signals datasets. After acquiring the data from an experimental setup under various speeds, a time-frequency-based Stepping Frame Synchrosqueezed Fourier Transform (SFSFT) is deployed in each segmented signal prior to feature extraction. The extracted features are used for training the Radial Basis Function Neural Network (RBFNN). Different balanced and imbalanced datasets are created to check the multi-fault classification ability of the proposed methodology. The results show that SFSFT-RBFNN is a generalised methodology, which performed tremendously well with a maximum classification accuracy of 100 % for balanced and imbalanced datasets in all three modality datasets. • Synchrosqueezed Fourier Transform is improved with stepping frame extraction method for multi-fault diagnosis. • Minute details of multi-fault signatures are extracted using SFSFT, and RBFNN is used for fault detection. • Performance of the proposed method is tested with balanced and different imbalanced datasets. • Methodology validation is carried out with acoustic, vibration and current datasets. [ABSTRACT FROM AUTHOR]

Inter-turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motor (PMSM) have gained significant attention due to the growing demand for enhanced reliability and safety in actuation systems. This paper presents a adaptive fault diagnosis approach specifically designed for ITSC faults in PMSM used in oil-drilling applications, where sparse actual data and varying speed conditions pose considerable sparse training data and ITSC feature extraction challenges respectively. To address these challenges, we first construct a physical fault model of PMSM-ITSC and establish a simulation experiment platform to replicate downhole environments with high temperatures and rapidly changing PMSM speeds, ensuring a reliable data source for analysis. Subsequently, we propose a novel adaptive peak-to-peak self-finding method (APPS) that leverages frequency-domain prior knowledge to adaptively extract ITSC fault characteristics, even amidst drastic changes in PMSM speed. Furthermore, we introduce the time-sequence efficient moving window self-attention network (EMWSAN) data model for inferring the stator phase winding state, incorporating Half-sandwich and Cascaded windows group attention operations. This approach significantly reduces computational complexity and network parameters compared to traditional self-attention mechanisms. To expedite the fitting process, we apply transfer learning (TL) theory, transferring knowledge from the physical knowledge to the data model, enabling EMWSAN to be trained more efficiently. As a result, our proposed ITSC fault diagnosis scheme achieves an impressive 96.72% classification accuracy, achieving existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The acoustic-based method is a prevalent way for non-contact fault diagnosis on gas-insulated switchgear. Gas-insulated switchgear always work under different voltages causing great diversity in acoustic frequency, which challenges robust fault detection. This paper proposed a novel feature-fused method to improve the robustness of fault detection on gas-insulated switchgear. The proposed method consists of four components: wave reduction module, spectrogram reduction module, fusion module and classifier. Wave reduction module extracts operating voltage information from acoustic emissions in the gas-insulated switchgear; spectrogram reduction module uses auto-encoder training schedule for feature extraction on spectrogram; fusion module fuses extracted features; classifier makes final classification. Also, we proposed an objective function for thoroughly utilizing spectrogram information. The efficacy of the proposed method was validated using experimental data from a real gas-insulated switchgear, and it shows competitive performance in fault detection compared to existing methods. • This work presents the procedure for implementing a fault detection method for gas-insulated switchgear using acoustic. • To improve the stability and the performance, we take gas-insulated switchgear working condition information into consideration. • We apply multi-stage pre-training into fault detection, making our method convenient to applied to new data with similar characteristics. • We introduce transformer based model into fault detection, making our model quite small but still high performance. [ABSTRACT FROM AUTHOR]

• A generation method of tokens sequence which can process 1D format data is presented. • A new diagnosis model based on the time series tokenizer and Transformer is proposed. • The effect of structural hyperparameters is discussed in detail. • A better feature vectors distribution form is verified via t-SNE. Fault diagnosis of rotating machinery is a significant engineering problem. In recent years, fault diagnosis methods have matured based on the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). However, these traditional models have the problem of Long-Term Dependencies, leading to their feature extraction ability defect. To address these issues, we proposed a new method based on the Time Series Transformer (TST) to recognize the fault modes of the various rotating machinery. In this paper, firstly, we design a new tokens sequences generation method that can handle data in 1D format, namely time series tokenizer. Then the TST combining time series tokenizer and Transformer is presented. The test results on the given datasets show that the proposed method has better fault identification capability than traditional CNN and RNN models. Secondly, the effect of structural hyperparameters on fault diagnosis performance, computational complexity, and parameters number of the TST is analyzed in detail through experiments. The influence laws of some hyperparameters are obtained as well. Finally, the feature vectors in the embedding space are visualized via the t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction method. On this basis, the working pattern of TST is explained to a certain extent. Moreover, we find that the feature vectors extracted by the proposed method show the best intra-class compactness and inter-class separability compared with CNN and RNN models by analyzing their distribution form, which further demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Multisensor information are usually required to recognize the health condition of machinery by domain experts, since redundancy and complementarity of multisensor information can enhance robustness of fault diagnosis. However, the mainstream approaches of intelligent fault diagnosis based on deep neural networks only focus on applications of single sensor. In this paper, a novel intelligent fault diagnosis model using mixture of Gaussians (MoGs) and variational auto-encoders (VAEs), named Mix-VAEs, is proposed to leverage redundancy and complementarity of multisensor information. The proposed Mix-VAEs mainly contains two modules. First, feature extracting module is constructed by several parallel and independent VAEs which are used to learn data representation from multisensor data respectively, and their latent variable distributions extracted by VAEs are used for fusion. Second, in multisensor information fusion module, a novel fusion method is proposed where latent variable distributions are combined to form MoGs, and the fused features are obtained in a sampling manner from MoGs. The performance of the proposed model is verified through two datasets consisting of multisensor data. Experimental results demonstrate the robustness of the proposed model in two scenarios, sensor failure and local signal missing, while also showing the better performance than other fusion models. [ABSTRACT FROM AUTHOR]

Morphological filtering has been extensively applied to rotating machinery diagnostics, whereas traditional morphological operators cannot effectively extract fault-triggered transient impulse components from noisy mechanical vibration signal. In this paper, a framework of generalized compound morphological operator (GCMO) is presented to enhance the extraction ability of impulsive fault features. Further, several new compound morphological operators are developed for transient impulse extraction by introducing the product, convolution, and cross-correlation operations into the GCMO framework. In addition, a novel strategy for selecting the structural element length is proposed to optimize the repetitive impulse feature extraction of the compound morphological operators. The fault feature extraction performance of the developed compound morphological operators is investigated and validated on the simulation signals and measured railway bearing vibration signals, and compared with the combined morphological operators and five existing feature extraction methods. The results demonstrate that the morphological cross-correlation operators are more efficient in repetitive fault impulse feature extraction and bearing fault diagnosis than the combined morphological operators and the comparison methods. • The concept of GCMO is proposed for the design of new morphological operators. • Three specific GCMOs based on the product, convolution and cross-correlation operations are proposed. • Several new compound morphological operators are proposed for impulse feature extraction. • The morphological cross-correlation operators exhibit better performance in bearing fault feature extraction. [ABSTRACT FROM AUTHOR]

Wind power generation has been widely adopted due to its renewable nature and decreasing capital cost per kW. However, existing equipment ages rapidly, leading to higher failure rates, greater operation and maintenance costs, and worsening safety conditions, calling for improved condition monitoring and fault diagnosis for wind turbines. Past methods utilize physical models, but they are only successful in laboratory environments. As increasing data are becoming available, there are methods applying machine learning without careful discrimination, leading to low accuracy. To solve this problem, first this paper proposes to conduct unsupervised learning to understand data properties, e.g., structural density. Subsequently, the sensitivity analysis is conducted to extract the significant features and to avoid overfitting. The sensitivity of various features that are characteristics of wind turbine bearings may vary significantly under different working conditions. During such a process, the piece-wise properties are studied to improve supervised learning. By combining the properties of data and regression, a three-stage learning algorithm is proposed to refine and learn the most useful information for turbine bearing fault diagnosis. The proposed framework is validated by using real data from diversified data sets for nonstationary vibration signals of bearings. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has been widely applied in process fault diagnosis due to its powerful feature extraction ability. A predominant property of these fault diagnosis models is to extract effective features from process signal. However, it is still difficult for them to construct the feature association relationship between input data. To solve these problems, this paper proposes a new graph neural network (GNN), pruning graph Convolutional network (PGCN), to perform feature learning based on the graph data. One dimensional process data are transformed into graph data by a graph construction method. A graph Convolutional network (GCN) is used to extract the features of process data. A pruning method of graph structure is proposed to effectively extract important information from process fault data. The feasibility and effectiveness of PGCN are verified on two benchmark processes, i.e., continuous stirred-tank reactor (CSTR) and fed-batch fermentation penicillin process (FBFP). The experimental results show that the performance of PGCN in feature extraction and process fault diagnosis is better than that of other typical methods, which provides a good possibility for the application of GCN in industrial process fault diagnosis. • A pruning graph Convolutional network (PGCN) is proposed to learn features from process variables. • The feature learning improves fault detection and diagnosis performance. • PGCN provides an effective way for process fault diagnosis. • The feasibility and effectiveness of PGCN are verified on two benchmark processes. [ABSTRACT FROM AUTHOR]

• This paper innovatively designs a diagnosis framework for aero-pipelines with a lightweight spatial-temporal model. • The lightweight spatial feature extraction module is designed to extract fine-grained features from fault data. • The bi-directional temporal feature extraction module is designed to extract coarse-grained features from fault data. • A multi-channel lightweight diagnosis model has been designed from the requirement of a lightweight computing process. • A novel intelligent model for end-to-end aero-engine pipeline system composite fault diagnosis. Accurate identification of faults in the aero-engine hydraulic pipelines is of great significance for condition monitoring of engines as well as safe and reliable operation of aircraft. However, due to the complexity of the structure of the aero-engine pipeline systems, the correlation of the coupling, and the multi-source nature of the excitation it is subjected to, the identification of failure characteristics has been an urgent problem in both engineering and academia. In addition, so there is no more qualitative formula or characteristic index to describe the fault of the aero-hydraulic pipeline. Therefore, to overcome these challenges, in this paper, a new compound faults diagnosis framework, namely Lightweight Spatial-Temporal Model Fusion Self-Attention Mechanism (LST-SATM-Net), is proposed for the aero-engine hydraulic pipeline systems. Firstly, the lightweight spatial feature extraction module is designed to extract fine-grained features from aero-engine hydraulic pipeline fault data and the bi-directional temporal feature extraction module is designed to extract coarse-grained features with temporal patterns from fine-grained features, achieving coarse and fine-grained feature fusion. Then, a self-attention mechanism is integrated into the spatial–temporal model for optimization, enabling the model to automatically learn important features from data with multiple sources of coupled vibration excitation, enhancing the performance of critical features from the correlated coupling of compound faults. The powerful noise interference is suppressed and the weighting coefficients are continuously updated to make the final decision more focused. Consequently, the recognition accuracy of the LST-SATM-Net model is improved and the feature learning of the neural network model is more flexible. The results demonstrated that compared with seven other advanced methods, the proposed LST-SATM-Net model is capable of diagnosing compound faults in the hydraulic pipeline system of aero-engine more accurately after being trained by experimental measurement data. This impressive finding indicates that the proposed LST-SATM-Net model can be applied to the actual condition monitoring of aero-engine hydraulic pipelines to reduce maintenance costs and downtime. [ABSTRACT FROM AUTHOR]

• MLERD obtains the optimal frequency band through multi-layer decomposition, so that fault features can be extracted. • ASWK is more sensitive to periodic pulses and is suitable for evaluating the fault feature information in components. • AMLERD can realize adaptive extraction of optimal mode component without setting parameters. Traditional signal processing methods cannot effectively segment the optimal frequency band, resulting in the fault characteristics are not obvious. Therefore, this paper proposes a multi-layer empirical Ramanujan decomposition (MLERD) method. In MLERD, the number of decomposed components is gradually increased through multi-level decomposition to ensure that the optimal frequency band can be obtained in the spectrum segmentation stage. Subsequently, the adaptive spectral weight kurtosis (ASWK) is defined to adaptively identify the optimal mode component. Compared with kurtosis, ASWK is more sensitive to periodic pulses and can better evaluate fault feature information. Based on this, this paper further proposes an adaptive multi-layer empirical Ramanujan decomposition (AMLERD) method, which can realize adaptive identification of the optimal frequency band and extract the optimal mode component of complex signals. The analysis results of roller bearing simulation and experimental signals show that AMLERD can effectively extract fault feature information and has excellent noise robustness. [ABSTRACT FROM AUTHOR]

• Extraction of multi-level CNN fault features is presented. • A unified feature space to form hyper features is presented. • Hyper features formed by feature fusion are proposed. Traditional feature extraction methods are highly dependent on manual work, and manual extraction will inevitably ignore some potentially useful features. For complex systems, there is no simple one-to-one correspondence between faults and symptoms, and fault characteristics often have complex characteristics such as hierarchy, propagation correlation, and delay. In the existing research results, the convolutional neural network (CNN) has been proven to be an effective network structure. But the existing network structure is more like a classifier, and only some features of industrial data are considered in the feature extraction stage, which will cause some feature information to be lost during the training process. To effectively solve the above problems, this paper proposes an industrial process-oriented hyper feature extraction network structure. This method first extracts multi-level CNN fault features, aggregates the hierarchical feature maps and compresses them in a unified feature space to form hyper features. Then the hyper features formed by feature fusion are input to the stacked Long Short Term Memory (LSTM) network for further feature extraction. In this way, the information in the time series can be effectively transmitted, and at the same time, the problem of gradient disappearance caused by long-term training can be solved. We conducted experiments on the Tennessee-Eastman (TE) benchmark process and industrial coking oven datasets. The experimental results show that the proposed method can effectively improve the fault diagnosis accuracy of the industrial production process system. [ABSTRACT FROM AUTHOR]

Recently, deep learning techniques have been widely applied to fault diagnosis due to their outstanding feature extraction abilities. The success of deep-learning-based fault diagnosis methods is highly dependent on the quantity and quality of the training data. In practical scenarios, it is challenging to obtain sufficient high-quality training data for fault diagnosis tasks due to complex environments, which would affect the effectiveness of the deep learning methods. In this paper, a new fault diagnosis method is proposed for motor bearing fault diagnosis under small samples. The Siamese neural networks (SNNs) are employed to extract the fault features. A multi-stage training strategy is proposed to train the SNNs with the aim to prevent the training stagnation problem and handle the small sample problem. A multi-source feature fusion network is developed to make full use of the multi-source sensor data by fusing the extracted fault features for further fault diagnosis. The proposed method is applied to motor bearing fault diagnosis on two real-world datasets. Experimental results demonstrate the effectiveness and feasibility of the introduced method for motor bearing fault diagnosis under small samples. [ABSTRACT FROM AUTHOR]

Data-driven fault diagnosis has become a hot topic of research in recent years, due to its wide applicability, high accuracy, and ease of modeling. In data-driven fault diagnosis, feature extraction is usually performed through a combination of one-dimensional time domain or frequency domain analysis methods. However, the constraints of data format and diagnostic methods give rise to three primary issues: inadequate utilization, expression, and mining of fault features, thereby impeding the broader application of data-driven methods. This paper presents a fault diagnosis framework that integrates cross-domain fusion images and a lightweight feature-enhanced network to address these issues. We leverage the temporal and frequency features of raw time series data, transforming them into images. This approach not only offers a greater amount of fault information but also highlights the inherent features more prominently. In terms of diagnosis methods, a novel feature-enhanced network model is proposed. This model takes images generated by a cross-domain image fusion module as inputs and then goes through a customized feature extraction module, a feature fusion module, a feature enhancement module, and a classification module to achieve high-precision and fast fault diagnosis. The effectiveness of this framework is validated on two publicly available datasets. The results show that the average accuracy of this fault diagnosis framework can reach 100%, surpassing the findings of existing research. The experiments on noise resistance further showcase the superiority of the proposed framework. It is expected that the framework presented in this study will provide a new perspective and direction for fault diagnosis. • Propose an innovative framework for precise fault diagnosis. • CFI images can translate fault information into texture and color features. • Differences between different faults can be better highlighted through CFI images. • Demonstrate the superiority of the framework through two distinct case studies. [ABSTRACT FROM AUTHOR]

Lempel–Ziv complexity (LZC), as a nonlinear feature in information science, has shown great promise in detecting correlations and capturing dynamic changes in single-channel time series. However, its application to multichannel data has been largely unexplored, while the complexity of real-world systems demands the utilization of data collected from multiple sensors or channels so as to extract distinguishable fault features for fault diagnosis. This paper proposes a novel method called multivariate multiscale dispersion Lempel–Ziv complexity (mvMDLZC) to extract the fault features hidden in multi-source information. First, multivariate embedding theory is applied to obtain multivariate embedded vectors and multivariate dispersion patterns, which can reflect the inherent relationships in the multichannel series. Second, by assigning labels to these patterns, the original multichannel time series can be transformed into a symbolic sequence with multiple symbols instead of the original binary conversion, enabling the accurate recovery of the system dynamics. Finally, the complexity counter value and normalized LZC are calculated for the complexity measure. Experimental results using synthetic and real-world datasets demonstrate that mvMDLZC outperforms existing LZC-based methods and multivariate dispersion entropy in recognizing different states of mechanical systems. Additionally, mvMDLZC exhibits robustness in handling challenges such as small sample datasets and noise interference, making it suitable for real industrial applications. These findings highlight the potential of mvMDLZC as a valuable approach for dissecting multichannel systems across various real-world scenarios. • Symbolic transformation improves multichannel time series analysis. • Proposed mvMDLZC extends LZC-based methods for multichannel systems. • The comprehensive comparative studies confirm mvMDLZC's effectiveness. [ABSTRACT FROM AUTHOR]

Induction motors are widely used in various industrial applications due to their simplicity, robustness, and high efficiency. In recent years, deep learning approaches have shown great potential for detecting and diagnosing faults in induction motors. However, it was observed that the convolutional neural network (CNN) might miss the crucial long-range temporal dependencies of the significant features. At the same time, the recursive neural network (RNN) may face issues such as vanishing gradient and less computational efficiency if the data is too long with complex patterns. This paper proposes a novel deep-learning model that combines a one-dimensional CNN and an RNN into two independent pipelines. The spatial, temporal, long-term, and short-term features can be extracted from the raw input signal. A multi-head mechanism, a state-of-the-art deep learning technique in natural language processing (NLP), enables the model to focus more on relevant features and prevent overfitting. Specifically, our method simplifies the conventional fault diagnosis workflow while maintaining competitive accuracy compared to several state-of-the-art methods. By leveraging the ability to detect faults directly from raw, unprocessed electrical signals, our approach offers a practical, cost-effective solution for real-time fault detection in industrial applications. This innovation has the potential to significantly reduce downtime and the associated operational costs, thereby providing immediate practical value in various industrial settings. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis plays an important role in maintaining the safe and reliable operation of rotating machinery. However, the data collected in real engineering scenarios may be severely insufficient, which presents challenges to the intelligent fault diagnosis methods. To address this problem, this paper introduces a metric-based meta learning approach for gear fault diagnosis under zero shot conditions. Firstly, a gear-rotor dynamics model is established to simulate the vibration signals under different fault conditions. And the signals are converted into energy maps through wavelet transformation to provide frequency domain fault features. Secondly, a deep convolutional network is employed as the feature extraction module to construct the prototype representations by calculating the average embedding within each fault class. Then, the distances between the actual signals collected from the gear test rig and the class prototypes are computed. Finally, the softmax is applied to convert these distances into probability distributions for outputting the predicted fault classes. Furthermore, label smoothing technology is introduced to mitigate the probability distribution differences between simulated signals and real signals. The experimental results demonstrate that the average diagnostic accuracy of the proposed model reaches 98.9%, which is better than other models. [ABSTRACT FROM AUTHOR]

How to extract the running feature information and realize multi-type faults diagnosis is the key to carry out intelligent operation and maintenance of energy conversion machinery. The pumped storage unit (PSU) has various operating conditions, both energy storage and power generation. It may lead to diversified types of failures under the joint influence of hydraulic and mechanical factors. The existing data-driven models often show excellent diagnostic performance with laboratory-specific fault types or single subsystem but are not satisfactory when applying real operating scenarios or migrating to other devices. Therefore, a universal hydraulic-mechanical diagnostic framework integrating signal acquisition, feature extraction and fault recognition is proposed in this paper. In Stage 1, eight types of datasets caused by the hydraulic-mechanical coupling characteristics via abnormal on-field measurements form an on-site benchmark; for Stages 2 and 3, a novel refined composite multiscale cosine similarity Lempel-Ziv complexity method is proposed to quantify the various fault features based on multiscale signal processing and the nonlinear dynamics methodology, and random forests model is introduced to realize the efficient recognition of different status signals. Its core advantage is versatility, which is not limited to specific components but can be applied to different subsystems of pumped storage, such as hydraulic system (identification of vortex conditions, detection of hydraulic imbalances) and mechanical system (wear of shaft, bearing and runner). This framework is applied in the micro PSUs, the comprehensive experiments show that all evaluation indexes are above 92%. Various comparative analysis indicates that the framework is not only applicable to the detection and analysis of hydraulic anomalies but also has a competitive advantage in the diagnosis of hydraulic-mechanical faults. It is suitable for fault detection of different subsystems in real power stations, and also could be flexibly extended to other rotating energy storage systems, as a helpful tool to improve energy conversion efficiency and reduce maintenance cost. [Display omitted] • Building a universal hydraulic-mechanical diagnostic framework. • Eight types of datasets caused by the hydraulic-mechanical coupling characteristics form an on-site benchmark. • Verifying the superiority of the framework by applying in the micro pumped storage system. • Providing a potential tool to improve energy conversion efficiency and reduce maintenance cost. [ABSTRACT FROM AUTHOR]

In recent studies, Transformer collaborated with convolution neural network (CNN) have made certain progress in the field of intelligent fault diagnosis by leveraging their respective advantages of global and local feature extraction. However, the multihead self-attention block used by Transformer and cross-channel convolution mechanism existing in CNN would make the collaborative models overly complex, leading to higher hardware requirements and limited industrial application scenarios. Therefore, this paper proposes a lightweight fault diagnosis framework called LiConvFormer to address the aforementioned challenges. First, a separable multiscale convolution block is designed to extract multilocal receptive field features of vibration signals and greatly reduce the learning parameters and computations. Second, a broadcast self-attention block is developed to capture critical fine-grained features within the signal's global scope, while avoiding cumbersome operations such as matrix multiplication and multidimensional exponentiation. Experimental results on three mechanical systems show that the proposed framework can accommodate advantages of lightweight and robustness compared to the recent Transformer and CNN-based fault diagnosis methods; moreover, the superiority of the above two blocks is also verified. The code library is available at: https://github.com/yanshen0210/LiConvFormer-a-lightweight-fault-diagnosis-framework. [ABSTRACT FROM AUTHOR]

• The artificial hummingbird algorithm (AHA) has been utilised to make the FMD adaptive. • The adaptive FMD boosts the adaptability of the input signals for refining the fault features extraction ability. • A new health indicator SIMI has been developed which characterizes the signal's periodic impulses. • The efficacy of the proposed methodology has been proved through the case study from industries. In this paper, a novel scheme for detecting bearing defects is proposed utilizing single-valued neutrosophic cross-entropy (SVNCE). Initially, the artificial hummingbird algorithm (AHA) is used to make the feature mode decomposition (FMD) adaptive by optimizing its parameter based on a novel health indicator (HI) i.e. sparsity impact measure index (SIMI). This HI ensures full sparsity and impact properties simultaneously. The raw signals are decomposed into different modes by adaptive FMD at optimal values of its parameters. The energy of these modes is calculated for different health conditions. The energy interval range has been decided based on energy eigen which are then transformed into single-valued neutrosophic sets (SVNSs) for unknown defect conditions. The minimum argument principle employs the least SVNCE values between SVNSs of testing samples and SVNSs of training samples to recognize the different defects in the bearing. The proposed methodology is applied to bearing from industrial mechanical systems. [ABSTRACT FROM AUTHOR]

Fault feature extraction is the basis of fault diagnosis. In the typical methods for AUV (autonomous underwater vehicle) thruster fault feature extraction, the local fault features of multiple input signals are extracted by using the modified Bayesian method, and the global fault feature is obtained by fusing these local fault features based on the evidence theory method. However, there are two problems based on the typical method. Firstly, there exists a large lag between fault detection time and occurrence time. Secondly, it is unable to extract effective global fault feature when the fault degree is low, that is, the global fault feature value is less than the interference. To solve these two problems, this paper develops the global fault feature based on two-stage fusion. Specifically in the first stage, multiple fault features based on thrust deviation are fused. In the second stage, local fault features are fused to obtain global fault features based on the cross-correlation method. The effectiveness of the proposed method is verified by AUV pool experiment. • Fault feature extraction method is proposed based on two-stage fusion from multi-source information. • The local fault features are extracted developed based on thrust deviation and multi-time window Fourier transform. • The third local fault feature is obtained based on cross-correlation. • Determine the fault occurrence time relatively accurately. • The ratio of fault features to interference features is high under weak fault conditions. [ABSTRACT FROM AUTHOR]

The photovoltaic (PV) array is the most sensible element in PV plants, which is subject to different type of faults and defects. Thus, to keep these plants working efficiently they should be monitored and protected carefully. Some faults if they are not detected and isolated promptly they may lead to hazardous risks. The diagnosis of PV systems is widely addressed and recently machine learning (ML) and deep leaning (DL) methods drawn the attention of many researchers. Most applications of ML methods are based on the use of the I–V curves measurement, as enough information and features can be extracted from the curves, to detect and classify faults. These methods showed their capability to classify some faults, like line to line, degradation, disconnected PV modules, partial shading effect, and bypass diode faults. Another approach is based on the use of thermal or electroluminescence images of PV modules/arrays to detect and identify defects, such as hot spot, snails crack, and others. In this paper, different ML and ensemble learning (EL) methods are evaluated for fault diagnosis of PV arrays. The focus is mainly on the detection and classification of some complex faults that may affect the PV arrays, i.e., multiple faults, and faults with similar I–V curves, that are not evaluated before. The results showed the ability of the methods developed to detect faults with very good accuracy (classification rate = number of classified instances/total instances), within 99%, while the classification faults is done with an acceptable accuracy, within 81.73%. Through this study it is shown when really ML and EL methods should be used, and some recommendations, challenges and future directions in this topic are presented. [ABSTRACT FROM AUTHOR]

Online diagnosis for sucker rod pumping well has great significances for rapidly grasping operations of the oil well. Feature extraction of the working condition and determination of the online diagnostic algorithm are two indispensable parts. In this paper, five feature vectors are extracted using Freeman chain codes. Then, an optimized density peak clustering (DPC) method is proposed to realize online diagnosis solved by an improved brain storm optimization (BSO) algorithm, in which the cloud model is adopted to generate new solutions in the searching space. During the online diagnosis process, a new cluster updating strategy is used to update the cluster centers online. According to the proposed online diagnostic method, various samples are automatically classified into different classifications by the unsupervised learning. The simulation results verify that the proposed online diagnosis method is satisfactory, which can give a higher and more stable diagnostic accuracy. • An 8-directions chain codes based feature extraction method is proposed. • An optimized density peak clustering algorithm is proposed. • An improved brain storm optimization algorithm based on the cloud model is proposed. • A new cluster updating strategy is proposed to update the cluster centers online. [ABSTRACT FROM AUTHOR]

Rolling bearing are one of the most important rotating components in the rotating machine and they are prone to failure due to complex harsh working conditions. However, it's a difficult issue to extract the fault feature from heavy noisy signal. In this paper, a new multilayer hybrid denoising method called local mean decomposition (LMD)-based enhanced sparse coding shrinkage (SCS) (named LMD-EnSCS) is proposed for impulse component extraction from bearing vibration signals. First, LMD is used to decompose the signal into a set of product functions (PFs) to enable impulse component as prominent as possible. Moreover, the multiple criteria is proposed to select the effective PF components. The rest filtering layer is used to enhance and extract the impulsive feature through minimum entropy deconvolution (MED) and sparse coding shrinkage (SCS) respectively. MED is able to enhance the impulsive component of noisy signal, which improve the impulsive extraction ability of SCS effectively. The simulation signal and bearing signal are used to verify the effectiveness and superiority of LMD-EnSCS, and the results show that LMD-EnSCS can extract the fault feature and perform well for bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

To increase the reliability of motor drive system, many fault diagnosis approaches have been reported with regard to three-phase Pulse Width Modulation Voltage Source Inverter (PWM-VSI). Based on feature engineering and deep neural network, this paper proposes a fault diagnosis approach for the VSI in three-phase Permanent-magnet Synchronous Motor (PMSM) drive. The three-phase current signals are used for the fault diagnosis of VSI. 10 typical signal features are extracted from the three-phase current signals and used as the input of deep neural network. To improve the diagnostic performance, the network structure of the presented deep neural network is designed like a pyramid. Experimental results show that the presented method can detect not only the single open-circuit faults but also the double open-circuit faults in power switches, with high diagnostic accuracy (more than 95%). Besides, comparison results show that the presented method has strong generalization performance. This paper provides theoretical guidance for the fault diagnosis of VSI in PMSM. [ABSTRACT FROM AUTHOR]

Applying effective supervised learning methods is difficult when dealing with complex industrial equipment such as Electrical Submersible Pump as the labeling work is costly and requires the dedication of an expert in the field. Moreover, the appearance of a new, unknown fault may require more time from the expert, not only for labeling the data but also for an arduous search for samples of this new type of fault. The solution developed in this paper uses uncertainty-based active learning for aiding experts through intelligent fault diagnosis and the search for potential samples of a new type of fault. The proposed approach was tested with a classical classification algorithm (random forest) applied to features extracted from the vibration signals. Two different feature extraction methods were investigated and contrasted: one using features defined by the expert and another using automatic feature extraction by a deep learning procedure. The algorithms were trained and tested on a dataset composed of 5616 vibration signals labeled in the frequency domain. The results show enough evidence to claim, at a significance value of 5%, that the active learning procedure proposed in this paper outperforms active learning based on random search. A new acquisition strategy for active learning is proposed to aid experts to find potential samples of a new fault. The recovered samples of the new fault aid improving performance of feature extractors based on deep learning since this type of method is highly affected by imbalanced data. • Active learning to enable class sample recovery of electrical submersible pump. • Active learning increases performance even with a restricted number of samples. • The proposed acquisition strategy better acquires samples of a certain class. • Proposed acquisition strategy reduces human effort on data for deep leaning models. [ABSTRACT FROM AUTHOR]

Acoustic signals have attracted considerable attention in mechanical fault diagnosis because of their advantages in non-invasive technique, instant measurement and low cost. However, traditional fault diagnosis methods could not achieve accurate feature extraction because of the strong noise environment of acoustic signals. In view of this, this study aims to provide a method that could accurately extract effectiveness features under noisy environment. Sparse representation is a research hotspot in intelligent fault diagnosis and has shown great power in feature extraction. In this paper, a novel fault diagnosis method based on parallel sparse filtering is presented to achieve sparse feature extraction from acoustic signals. Specially, parallel sparse filtering achieves sparse feature exaction by adding another normalization direction based on sparse filtering, and the derivation of parallel sparse filtering is also presented in detail. Then Z-score normalization is used to activate the training and testing data in fault classification process. The superiority of the proposed method is validated by simulated and experimental data. The results show that PSF is a promising sparse feature extraction method that can be used for mechanical fault diagnosis under acoustic signals. [ABSTRACT FROM AUTHOR]

This paper uses wavelet neurons instead of traditional neurons, and uses wavelet multi-resolution analysis to decompose the FPGA image scan of the ship engine. Because the neural network has the approximation ability of arbitrary functions, the wavelet transform is connected to the neural network to form a wavelet neural network. To test ship engines. The hardware design of GigE image acquisition and processing system based on FPGA was started. FPGA was used as the main control chip and Gigabit Ethernet was used as the transmission medium. The hardware circuit of the image data acquisition and image processing system was designed. It mainly includes the FPGA main control circuit and the FPGA Peripheral circuits. The high-speed image acquisition, transmission, storage, and display module circuit design is realized. Real-time monitoring and fault analysis of the engine's condition is performed by the FPGA image scanning method, and data of the engine's running state is pre-processed with the help of step tracking technology to make it a standard signal. The data is transmitted to the computer through NI's data acquisition card. Combining feature extraction such as information entropy, Fourier transform, EMD and wavelet neural network technology. The accuracy of the diagnosis results and the actual fault state is improved. It can enable the staff to monitor the running status of the engine in real time, improve the efficiency of engine fault diagnosis, reduce labour costs and maintenance costs, and thus realize intelligent, real-time and accurate status monitoring of the engine. [ABSTRACT FROM AUTHOR]

The rolling bearing vibration signals are complex, non-linear, and non-stationary, it is difficult to extract the sensitive features and diagnose faults by conventional signal processing methods. This paper focuses on the sensitive features extraction and pattern recognition for rolling bearing fault diagnosis and proposes a novel intelligent fault-diagnosis method based on generalized composite multiscale weighted permutation entropy (GCMWPE), supervised Isomap (S-Iso), and marine predators algorithm-based support vector machine (MPA-SVM). Firstly, a novel non-linear technology named GCMWPE was presented, allowing the extraction of bearing features from multiple scales and enabling the construction of a high-dimensional feature set. The GCMWPE uses the generalized composite coarse-grained structure to overcome the shortcomings of the original structure in multiscale weighted permutation entropy and obtain more stable entropy values. Subsequently, the S-Iso algorithm was introduced to obtain the main features and reduce the GCMWPE set dimensionality. Finally, a combination of GCMWPE and S-Iso set was input to the MPA-SVM for diagnosis and identification. The marine predators algorithm (MPA) was used to obtain the optimal SVM parameters. The effectiveness of the proposed fault diagnosis method was confirmed through two bearing fault diagnosis experiments. The results have shown that the proposed method can be used to correctly diagnose bearing states with high diagnostic accuracy. [Display omitted] • An intelligent fault diagnosis method is presented for rolling bearing. • Modified multiscale weighted permutation entropy is proposed for feature extraction. • Marine predators algorithm is used to optimize support vector machine parameters. • Bearing fault diagnosis cases are employed to test the presented method. [ABSTRACT FROM AUTHOR]

• The concept of generalized combined morphological operators is proposed. • Typical combined morphological operators are investigated and compared. • MHCCO is proposed for extracting bearing fault-induced repetitive transients. • An enhanced morphological filtering method is proposed for bearing diagnostics. • The proposed method exhibits excellent bearing fault diagnosis performance. Morphological filtering adopting the combined morphological operators (CMOs) has been widely used to extract bearing fault features from vibration signals. However, few studies focus on the comprehensive performance of various CMOs under different interferences. In this paper, several new CMOs for feature extraction are proposed firstly, and then the impulse extraction performance of fourteen typical CMOs in the presence of harmonic interference, random impulses and background noise is investigated through simulations. To enhance the capability of impulse extraction and noise elimination, the morphological hat cross-correlation operator (MHCCO) is constructed through the cross-correlation of two CMOs with excellent performance. Additionally, an improved strategy is proposed to adaptively determine the optimal length of the structural element for MHCCO. Simulations, experiments and comparisons demonstrate the effectiveness and superiority of the proposed method. This paper provides important guidance for selecting CMO for feature extraction and an effective method for bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

• The VSI refines the features to expand the distinction between different faults. • The GaCF guides the model to extract multi-scale and multi-directional features. • ADPooling facilitates the learning of critical features and suppresses feature decay. • The DGCAPN is beneficial to increase the diversity and recognition of features. • The proposed method improves the accuracy and robustness of fault diagnosis. To address the limitations of insufficient feature representation and easy to be overwhelmed by strong noise in the grayscale images of vibration signals, the random generation and single structure of convolutional kernels of the convolutional neural network leading to insufficient extracted features, and the max pooling and average pooling leading to model overfitting and suppression of critical fault features, this paper proposes a novel diagnosis method based on signal-to-image mapping and deep Gabor convolutional adaptive pooling network. Firstly, this paper designs a vibration signal-to-image mapping strategy to highlight the fault information of vibration signal. Then Gabor convolutional filter is proposed instead of convolutional kernels to guide the model to extract multi-scale and multi-directional fault features. Next, the dynamic adaptive pooling is designed to facilitate the retention of local features and suppress the decay of critical features. Finally, a deep Gabor convolutional adaptive pooling network model is constructed to improve the robustness of the fault feature extraction process and the generalization of the model. The results of the bearing and gear datasets indicate that the proposed method enhances the feature extraction ability, improves the robustness and generalization of the model, and realizes highly accurate fault diagnosis. [ABSTRACT FROM AUTHOR]