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• The gated recurrent unit algorithm with fused attention mechanism is a target positioning method in a non-line-of-sight error environment. • The introduction of the attention mechanism can analyze features, select important features, and better handle non-line-of-sight errors. • Introducing the dropout layer during training reduces the risk of overfitting during training. • Through simulation and actual measurement experiments, the effectiveness of the algorithm in this paper is verified by comparing it with other famous algorithms. Amidst the increasing reliance on indoor positioning in areas such as large shopping malls, airports, and train stations, the precision of ultra-wideband (UWB) technology stands out for its centimeter-level accuracy. However, non-line-of-sight (NLOS) errors caused by people's movements significantly reduce indoor positioning accuracy. In response, this paper presents an innovative blend of gated recurrent unit algorithm with fused attention mechanism (GRU_Attention). This algorithm adeptly captures the dynamism in UWB signals and intelligently focuses on pivotal features to actualize accurate three-dimensional positioning while effectively reducing NLOS error. It has been demonstrated that GRU_Attention algorithm can reach 5.6 cm accuracy level through experiments, which improved by 74.47 %, 87.44 %, 14.46 %, 43.82 %, and 29.07 % respectively compared with the backpropagation (BP), least square (LS), GRU, convolutional neural network (CNN), and fused attention mechanism convolutional neural network (CNN_Attention) algorithms. This algorithm charts a new pathway to overcome NLOS errors in indoor personnel movement scenes. [ABSTRACT FROM AUTHOR]

• A novel general cascaded convolutional neural network (CNN) reading method for pointer reading is presented. • Proposes PMA method, which can increase the data samples significantly without introducing redundant background information. • Proposes ADFPN for feature fusion, learns branch importance, performs fusion at various levels. Enhances fusion efficiency. • Construct a dataset for pointer meter readings that contains images of common pointer meters in substations. Pointer meters continue to serve as the primary monitoring instruments within substations. To solve the issue of poor model accuracy and low reading accuracy when reading multiple types of pointer meters, this paper proposes a universal cascaded convolutional neural network for accurate reading of various types of pointer instruments. The proposed detection method consists of two stages: instrument digital positioning and instrument pointer fitting. In the digital(figure of a number) positioning stage, an instrument data augmentation method for "large background and small target" is proposed to improve the model's sensitivity to digital position and reduce the interference of redundant background information to the model. In addition, an adaptive feature fusion pyramid structure is proposed for more effective feature fusion and enhanced fuzzy small target recognition. Compared with the baseline model, the method introduced in this study realizes a 0.47 % improvement in target detection accuracy. In our proposed dataset, the average recognition accuracy for the digital position location phase is 98.65 %,the average reading errors in square instruments, circular instruments, and instruments with uneven scales are 0.76 %, 0.73 %, and 0.67 % respectively. The proposed universal detection method in this paper achieves ultra-high precision and can be applied to reading pointer instruments of different types and scales. Compared with existing pointer meter reading methods, the proposed detection method showcases superior simplicity and effectiveness. Furthermore, it showcases a reduced measurement error, thereby bolstering its practical utility in industrial contexts. [ABSTRACT FROM AUTHOR]

• The initial measurement point is optimized, and the wavelet packet decomposition method is used to decompose and reconstruct the original measurement signal to realize the preprocessing of the data. Its main contributions include removing the DC component of the signal and reducing the non-stationarity and inconsistency of the sampled signal. • In view of the mode aliasing phenomenon caused by using EMD mode to analyze the signal, this paper uses VMD self-use segmentation technology to realize the adaptive segmentation of each component in the signal field, which effectively avoids the mode aliasing phenomenon, has stronger robustness, and effectively reduces the endpoint effect. At the same time, aiming at the problem that the grayscale map is not enough to visually express the deep features in the process of signal conversion to a grayscale map, this paper uses an SDPI diagram to display signal characteristics, and uses information entropy (IE) to select the optimal component after VMD decomposition to enhance the characteristics of symmetry point mode image (SDPI), and finally fuses the signal corresponding to the optimal component to generate a snowflake map, so as to display the changes between different vibration states simply, clearly and intuitively, and enhance the recognition ability of the image, and the image accuracy of the method is higher than the traditional sample accuracy. • Aiming at the phenomenon that the traditional SoftMax classification ability in convolutional neural network is not high, firstly, the convolutional neural network optimization is realized by adjusting the parameters of the CNN model, adding regularization, adjusting the hyperparameters, etc., and at the same time, the combined model combining SVM and CNN is used to replace the SoftMax classifier with the SVM classifier, and the results show that the method can improve the classification ability of images. • To verify the effectiveness and superiority of the proposed method, the proposed method is compared with the 1DCNN method, grey image-CNN method, SDPI-CNN, SDPI-SVM, and other methods, and the comparative results show that the model has good predictive performance and generalization ability, and verifies that the proposed signal picture establishment method and network improvement optimization model method are feasible for lead screw fault classification. The failure of the ball screw in the machine tool presents various types and complex coupling characteristics, which pose challenges in extracting fault features from vibration signals and lead to decreased accuracy in fault diagnosis classification. To tackle this issue, an improved intelligent fault diagnosis method based on enhanced CNN is proposed by optimizing the variational mode decomposition and symmetrized dot pattern image features. Initially, VMD is employed to decompose the preprocessed signals, incorporating information entropy for selecting decomposition layers to enhance accuracy. Different image features are generated using SDPI, and differential information is introduced to enhance feature expressive capability through fusion. Subsequently, considering the limited classification ability of traditional SoftMax classifiers in CNNs, SVM is integrated for classification purposes to optimize convolutional neural networks' structure and improve their classification performance. Finally, the proposed method was ultimately validated by means of comparative analysis of screw vibration signals, thereby affirming its exceptional accuracy in fault diagnosis and classification. [ABSTRACT FROM AUTHOR]

• Four different types of melt pool signals are integrated and fused to enhance the signal-to-defect correlation. • A data partitioning method is proposed to reduce registration errors and facilitate the defect-related feature extraction. • The feature-level fusion obtains the optimal classification performance compared with the other two fusion strategies. • The proposed data fusion method outperforms the individual signal-based methods for quality classification. In situ monitoring technologies for additive manufacturing are severely affected by signal-to-defect spatial registration, and a single monitoring solution that captures a specific process signature cannot identify defects with sufficient accuracy in industrial applications. In this paper, a data partitioning method was proposed to reduce registration errors and facilitate the extraction of defect-related representative features from data segments. Then, three multi-signal fusion methods based on convolutional neural networks were established from feature-level fusion, decision-level fusion, and data-level fusion, which enhanced the signal-to-defect correlation by means of complementary sensor information. The results showed that the feature-level fusion obtained the optimal classification performance compared with the other two fusion strategies. Among them, the classification accuracies of 100-, 150-, 200-, 300-, 400-, and 500-μm defects reached 74.41%, 89.84%, 90.82%, 93.95%, 97.85%, and 98.83%, respectively. The proposed data fusion approach outperformed the individual signal-based models for quality classification. [ABSTRACT FROM AUTHOR]

[Display omitted] • Aerial thermographic inspection is done by thermal cameras embedded in UAVs. • A new approach based on image analysis with two consecutive Neural Networks. • Detect thermal patterns associated with superficial faults in an IoT platform. • A real case study is proposed with thermograms from three PV solar plants. • Accuracy of 99 % for PV panel detection, and 96 % for hot spot detection. • False positives are reduced in comparison with other studies. Aerial thermographic inspection is performed with thermal cameras embedded in unmanned aerial vehicles, being one of the most relevant monitoring techniques for photovoltaic panels. This technique allows the detection of thermal patterns associated with faults in the photovoltaic panels, although image analysis and fault detection require novel processing methodologies. The volume and variety of aerial thermal data together with the need for faster and more efficient analysis for early maintenance operations require efficient Internet of Things architectures to provide fast and effective diagnostics with easy access. This paper presents a new approach based on image analysis with two consecutive convolutional neural networks to detect hot spots in an Internet of Things platform and reduce the number of false positives. The architecture of the platform is designed to automatically process the received data through the implementation of different Convolutional Neural Networks. A real case study is proposed with thermograms from three photovoltaic solar plants with different sizes, shapes of panels and a wide temperature range. The results show an accuracy of 99 % for panel detection and 96 % for hot spot detection with a reduction of false positives compared to other studies, such as Support Vector Machine or different Artificial Neural Networks, demonstrating the robustness of the method. [ABSTRACT FROM AUTHOR]

• A novel methodology integrating percussion and CNN model is established to detect concrete voids. • The CNN model trained by both CWT and MFCC show satisfying categorizing performances. • The methodology proves its good noise-immune capacity in processing percussion sound signals. • The superiority of the proposed method is well demonstrated by comparison with ML methods. As a fundamental construction material, concrete commonly encounter void defects, which could significantly impair the integrity of concrete members and seriously weaken their load-bearing capacity. Worse still, void defects typically exist inside concrete members, making it difficult to detect them in real time. Therefore, conducting regular inspections for internal void defects of concrete structures is of great importance to maintain structural health. This paper investigates an integrated method to detect concrete voids based on percussion approach and a convolutional neural network (CNN). Firstly, percussion-induced sound signals were collected from 18 concrete specimens with different precast void defects. Each specimen was tapped 100 times, for a total of 1,800 percussion-induced acoustic signals. Subsequently, continuous wavelet transform (CWT) and Mel frequency cepstral coefficient (MFCC) were introduced to convert the collected acoustic signals into two types of images. The converted images were divided into testing and training datasets according to the ratio of 1:3, and 600 images were input to the CNN as the test dataset and 1200 images as the training dataset. The CNN was then trained and verified by the joint input of CWT and MFCC images to perform concrete voids classification. The result shows that the proposed method presents a satisfying classification accuracy of 100 %. Next, percussion tests under 5 levels of noisy environments were conducted, and the results prove the remarkable anti-noise performance of the methodology by achieving 88.12 % accuracy even at the strongest signal-to-noise ratio of 0 dB. Finally, the superiority of the proposed method was further demonstrated through the accuracy comparison with machine learning (ML) methods, with the accuracy consistently outperforming over 25 %. In conclusion, the proposed method has a great potential for future in-situ applications in concrete void detection. [ABSTRACT FROM AUTHOR]

• A new blind T60 estimation method using a denoising-aided multi-task learning network is proposed for accurate T60 estimation. • The noise-robustness and applicability of the proposed model are comprehensively investigated. • Audio datasets with unknown RIRs and noise recorded in real-world classrooms are used to further validate the effectiveness of the proposed method. The noise in reverberant speech severely limits the estimation accuracy of reverberation time T60 using current deep learning (DL) methods. To address this issue, this paper proposes a denoising-aided multi-task learning (DAMTL) method for blind T60 estimation. Specifically, speech denoising, as an auxiliary module, is conducted joint training with T60 estimation for more accurate prediction accuracy. These two tasks are integrated into one DL network by sharing the same encoder network, where the complex-valued spectrum is introduced to extract comprehensive high-dimensional features from noisy reverberant speech. Subsequently, complex operation of 2-D convolutional neural network (Conv2d), batch normalization and long short-term memory (LSTM) are formulated. Furthermore, the noise robustness and applicability of the DAMTL are fully discussed by comparison with state-of-the-art DL-based methods using simulated data and real-world recorded data. The results prove the effectiveness and superiority of the proposed DAMTL, especially in low signal-to-noise ratio (SNR) scenarios and practical applications. [ABSTRACT FROM AUTHOR]

• An intelligent analysis framework of the super-large diameter shield machine performance is established. • The key parameters which affect the TAS (tunnelling advance speed) of shield machine are obtained. • Compared with traditional GRU and LSTM deep learning prediction models. The tunnelling advance speed (TAS) of a super-large-diameter tunnel boring machine (TBM) significantly affects project progress and safety. Effective prediction of the TAS of a TBM is crucial for early warning and evaluation of its excavation performance. This paper proposes a model based on convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) to predict TAS. This model can more effectively extract and retain many features; additionally, it considers the temporal relationships among the operating parameters of the TBM. Additionally, a Kalman filter (KF) is incorporated into the model to effectively remove input parameter noise, leading to a more accurate prediction of TBM excavation speed. A case study indicated that the pressure of the excavation chamber and the pitch angle of the shield tunnelling machine were correlated with the advance speed. The research results show that the proposed model can accurately and effectively predict TAS. [ABSTRACT FROM AUTHOR]

Convolutional neural networks have been widely utilized in industrial soft sensor modeling because of its excellent feature extraction capabilities in recent years. However, industrial data are often multimodal and contain noise, which affects the feature extraction capability of soft sensor models and leads to potentially large errors in the final key variable results. The solution to the above problems is based on denoising techniques and higher-level feature extraction techniques. Therefore, this paper proposes a Bayesian optimized Multi-Attention deep residual shrinkage network for industrial soft sensor model, referred to as BO-MADRSN. Firstly, in order to filter the noise in the original data during feature extraction, we used a soft thresholding method, where the thresholds are determined by back-propagation algorithm. Secondly, in order to retain the information of the data as much as possible and reduce the cases of zero after soft threshold filtering, we construct a residual network-like structure to complement the results obtained after soft threshold noise reduction by performing attentional feature extraction on the original features through the shortcut connections of this structure. Thirdly, we perform hierarchical denoising by stacking multiple residual blocks, and this structure facilitates network training. In addition, a Bayesian optimization strategy is introduced to select the optimal hyperparameters. In this paper, the results on several real industrial datasets verify the effectiveness and superiority of the proposed soft sensor model. • Soft threshold function used to solve the problem of data containing noise. • Multi attention fusion to solve the problem of soft threshold changing data to 0. • Bayesian optimization strategy for finding network parameters. [ABSTRACT FROM AUTHOR]

• DMU-TransNet is a novel anomaly detection and localization model. • ConvTR module is integrated into the encoder of DMU-TransNet to better reconstruct image. • DMSC module can detect anomalies of various scales and types with low computational cost. • MF module can minimize information loss and enable the model to obtain diverse information. • Our AUROC improves by 3.8% and 1.9% on the medical dataset and the MVtec-AD dataset. Anomaly detection is crucial in medical and industrial sectors. Despite the promising results of Convolutional Neural Networks (CNNs), existing approaches often focus predominantly on local visual features, neglecting global and multi-scale features. This limitation leads to unstable training and suboptimal detection performance. To address these challenges, this paper introduces a novel anomaly detection model, named DMU-TransNet. Firstly, the Convolution with Transformer (ConvTR) module is designed to enlarge the receptive field and enhance the model's global dependence. Secondly, the Dense Multi-scale Skip Connection (DMSC) module amalgamates multi-scale features from various layers, enabling the model to comprehend local details and global context simultaneously, while mitigating the vanishing gradients problem. Finally, the Multi-level Fusion (MF) module merges multi-scale features from multi-level decoders, minimizing information loss and enabling effective capture of diverse information. Extensive experimental results confirm that our method achieves optimal performance on several mainstream datasets. Our code is available at https://github.com/ml-AD/DMU-TransNet. [ABSTRACT FROM AUTHOR]

• Established a twin model for temperature and stress monitoring in LNG storage tanks. • Integrate artificial intelligence and digital twin technology to achieve LNG tank leak diagnosis. • Integrating Seq2seq, CNN, and LSTM to achieve spatiotemporal prediction of leakage areas. Temperature and stress serve as crucial indicators for monitoring the health of LNG storage tanks. To address the limitations of traditional point-based monitoring and transition to comprehensive digital monitoring, this paper introduces the application of digital twin (DT) technology. The DT model plays a pivotal role in accurately representing the state of the physical entity and serves as the foundation for monitoring services and meeting application requirements within DT systems. This article introduces a numerical simulation method guided by temperature sensor data. It utilizes a temperature and stress calculation program as a platform to create twin models for real-time assessment of the temperature and stress within the storage tank. By comparing with the Fluent simulation of physical field leakage, the DT model realizes real-time inversion of the temperature field and stress field from sensor data to the whole region. To enhance the diagnostic and predictive capabilities of the DT system, we flatten the computed temperature nephogram from the DT model and employ machine vision techniques to extract cloud map features. Machine learning methods such as support vector machine (SVM) and linear regression are utilized to achieve leakage diagnosis, leakage volume calculation, and leakage location calculation. Additionally, we introduce the sequence-to-sequence (Seq2seq) framework and combine it with convolutional neural networks (CNN) and long short-term memory (LSTM) to train the time series of nephogram, enabling spatiotemporal prediction of leakage areas. [ABSTRACT FROM AUTHOR]

• A novel spatiotemporal deep learning is proposed to detect abnormal sensor data. • The first layer of 1DCNN model is replaced by wavelet scattering transform. • The bidirectional LSTM is developed to recover abnormal sensor data. • Different sensor configurations are considered to select the optimal sensors. • Simulated and actual engineering data are adopted to validate the methodology. This paper proposes a novel deep-learning-enabled framework for abnormal data detection and recovery considering spatial-temporal correlation among sensors. The wavelet scattering transform is adopted to replace the first layer of one-dimension convolutional neural network classification model. The multiple typical sensor failure modes of sensor are identified including bias, drifting, gain, precision degradation, and three complete faults (constant; constant + noise; noise). The bidirectional long-short time memory (BLSTM) network is developed to recover abnormal data through positive and negative propagation. The BLSTM cell involving forget, input, and output gate is utilized to adaptively transmit valuable information of long-term data. Different sensor configurations are conducted to select the optimal input sensors of BLSTM model. The proposed models are then validated by three engineering cases. The results show that the failure mode diagnosis and recovery accuracy are over 90 %. Sensor selection has a large impact on the data reconstruction, and selecting sensors with higher correlation can achieve a higher accuracy. In addition, the data recovery accuracy of the proposed BLSTM model surpasses that of traditional long-short time memory models. [ABSTRACT FROM AUTHOR]

• A high accuracy fault diagnosis scheme for harmonic reducer practical application scenarios is proposed. • Dynamic rules for harmonic reducer in dynamic operating conditions are quantifiably expressed. • Behavioral model is constructed based on the mapping relationship between external excitation and monitoring signals. • The interpretability of Hidden Markov Model in fault diagnosis application is improved. Harmonic reducer is a critical and vulnerable component of industrial robots. Its dynamic rules are difficult to express because harmonic reducers always operate under continuously varying operating conditions in practical application scenarios. How to achieve fault diagnosis under dynamic operating parameters is a challenge. This paper proposes a fault diagnosis scheme for harmonic reducer under practical operating conditions. Hidden Markov Model is used to depict the dynamic rules, novel features state transition probability and observation probability are extracted to construct the mapping relationship between external excitation and monitoring signals. A CNN framework is employed for fault recognition based on fault impacts on the mapping relationship. The results of the verification experiment show that the scheme can achieve high accuracy fault diagnosis in dynamic operating conditions by fully utilizing Hidden Markov Model. The mapping relationship can serve as a behavioral model to support digital-twin modeling and health management for industrial robots. [ABSTRACT FROM AUTHOR]

• A hybrid deep learning model, i.e., CNN-LSTM is presented for the quantitative detection of polygonal wheels. • The hybrid model makes up for the deficiency of a single model and improves the reliability and generalization performance. • The effectiveness and robustness of the proposed model are proved under different running speeds and SNRs. • Both dynamics simulations and field tests are carried out to show the proposed method has high detection accuracy. Wheel polygonization poses severe threat to the operational safety of railway vehicles. To improve running safety, condition monitoring of wheel polygonization based on axle box acceleration (ABA) signals is an effective method. However, the signals usually contain strong noise, making it difficult to diagnose polygonal wheels quantitatively. To address these issues, this paper presents a quantitative detection method for wheel polygonization based on a deep learning (DL) model that integrates Convolutional Neural Network (CNN) with Long Short-term Memory (LSTM). The CNN is responsible for automatically extracting the sensitive spatial features from the original input and it can retain effective signal components. The LSTM is introduced to fuse the local features and conduct sequence modeling for polygonal wear amplitude estimation. Both dynamics simulations and field tests are carried out to verify the feasibility of the proposed method. The detection results indicate that the proposed model can accurately estimate the wear amplitudes. [ABSTRACT FROM AUTHOR]

Fault diagnosis plays a crucial role in ensuring industrial safety and enhancing social benefits. However, identifying fault paths accurately becomes challenging due to process coupling and complex flow. This paper proposes a data space-based approach for fault diagnosis and path recognition. It involves constructing a multi-tiered, multi-causal data space by extracting causal relationships from data. An attention-based convolutional neural network efficiently captures the causal relationships. Pruning and expert knowledge contribute to an elaborate structure. The fault propagation direction is determined using LSTM-least-square linear (OLS) method, aiding rapid problem location. Experimental verification on float glass production and the Tennessee Eastman demonstrates remarkable results, supporting improved reliability and efficiency in industrial processes. • A fault diagnosis and propagation path identification method based on data space is proposed, which can accurately identify the fault propagation path and root cause. • Proposing the construction of a multi-level, multi-causal data space to precisely identify direct causal relationships and eliminate indirect causal links. Multi-level causal diagrams offer enhanced interpretability for diagnosing fundamental reasons in practical production processes. • Introducing a data-driven fault propagation path identification method that efficiently eliminates redundant connections. It determines the propagation direction by assessing the strength of causal relationships between upstream causal variables and downstream affected variables. • The experiment of anomaly location tracing is carried out using the actual production process of float glass, and the effectiveness of the method is verified. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel neural network architecture called CTCNet. First, we adopt a multi-scale convolutional neural network (MSCNN) to extract low and high-frequency features, adaptive channel feature recalibration (ACFR) to enhance the model's sensitivity to important channel features in the feature maps and reduce dependence on irrelevant or redundant features, a multi-scale dilated convolutional block (MSDCB) to capture characteristics of different types among feature channels. Second, we use Transformer to extract global temporal context features. Third, we employ capsule network to capture spatial location relationships among EEG features and refine these features. Besides, the capsule network module is used as our model's classifier to classify the final results. It is worth noting that our model better solves the problem that previous researches failed to take into account the simultaneous extraction of local features and global temporal context characteristics of EEG signals, and ignored the spatial location relationships between these features. Eventually, we assess our model on three datasets and it achieves better or comparable performance than most state-of-the-art methods. • The proposed method extracts the local and global features of EEG signals. • We design a multi-scale CNN to extract different frequency features of EEG signals. • We use Transformer to capture global temporal context information of EEG signals. • We use capsule network to extract spatial relationships between EEG features. • The accuracy on Sleep-EDF-20 is 86.2%, on Sleep-EDF-78 is 82.5% and on SHHS is 85.7%. [ABSTRACT FROM AUTHOR]

Detecting pavement cracks is a complex task in pavement maintenance. Convolutional neural network (CNN)-based approaches have been increasingly used for this purpose in recent years, but these approaches still have limitations, such as insufficient processing of local features, poor boundary detection ability and information loss issue. In this paper, a novel CNN-based model, named APF-Net, is presented. Specifically, to extract richer and more feature information, proposed model utilizes a progressive fusion module (PF) that enables effective feature enhancement by feature learning from cross-layer features, which operates progressively across adjacent network layers, enabling the crack segmentation model to extract more global and detail features from the crack images. Additionally, a hybrid multiple attention (HMA) module that incorporates both a spatial attention mechanism from two directions and a channel attention mechanism, is proposed, which could effectively capture the long-term dependence of crack features and enhance crack boundary detection. Moreover, proposed model employs a lightweight feature extraction block, named mobile inverted bottleneck convolution (MBC) block, which significantly reduces the model parameters and enables more efficient processing of the crack images. This approach results in a reduction of the computational load and faster detection of pavement cracks. The effectiveness and generalization of the proposed APF-Net have been evaluated through experiments on three publicly available crack datasets. The results demonstrate that the proposed model outperforms other state-of-the-art segmentation models in detecting pavement cracks while with 7.6M parameters and 5.0G FLOPs. Our proposed model can contribute to the development of automatic pavement crack detection systems, which can aid in the maintenance and preservation of roads and highways. The source code for proposed APF-Net is available at https://github.com/MMYZZU/APFNet.git. • An attention-based progressive fusion network is proposed for end-to-end pixelwise crack detection. • A lightweight encoding path is proposed for feature representation through neural architecture search. • A progressive fusion block is proposed for effective feature enhancement to obtain more global and detail features. • A hybrid multiple attention block is proposed to enhance crack boundary detection ability. [ABSTRACT FROM AUTHOR]

In motor drive system, Bearing fault detection through motor current signal (MCS) analysis has gained recognition for its cost-effectiveness and non-invasive nature. However, two-dimensional (2D) convolutional neural networks (CNNs) exhibit suboptimal performance when directly applied to raw stator current signals for fault detection. Meanwhile, the training of Recurrent Neural Networks (RNNs) for processing one-dimensional (1D) temporal sequence can be a time-consuming task due to the inherent procedures of the network structure. To balance time-consuming and accuracy, this paper presents a novel MCS-based bearing fault detection model utilizing a 1D CNN and corresponding pre-processing methods. In the early stages, a noise cancellation strategy termed time shifting is implemented. Subsequently, wavelet transform is employed to further refine the processed signal by eliminating components with minimal significance. By compressing homogenous signals, the resultant tensor, referred to as the "root map", is employed as the input for the proposed temporal causal network. Two groups of experimental results conducted on both laboratory and industrial conditions validate the effectiveness and convenience of the proposed methodology. • Bearing fault detection through motor current signals is considered cheap and non-invasive. • Deeper networks for extracting fault components from raw current signals leads to overfitting. • Causal CNNs are one-dimensional networks, which can process data in parallel. • Equipment paired with the motor adds noise, hindering fault identification. [ABSTRACT FROM AUTHOR]

• Multi-source monitoring for quantitative diagnosis of CFRP fatigue cracks. • Quantitative method has combined the global guided wave and local FBG strain. • A heterogeneous feature fusion strategy of "image square" is proposed. • The accuracy of quantitative crack diagnosis models of 1D and 2D CNN are compared. In this paper, a quantitative method for fatigue crack diagnosis of composites is proposed, which combines guided wave and strain multi-source heterogeneous feature fusion with deep learning. Two types of sensors, i.e. piezoelectric sensor and fiber Bragg grating sensor, are used to measure the global and local response of composites under fatigue loading. The one-dimensional signal of the guide wave is encoded into an image by the Gram angular field transform method. The deep features of the image are further extracted by the AlexNet deep learning model. Furthermore, a heterogeneous feature fusion based on image square array of the guided wave and strain is presented. Finally, the CNN models with different input features are constructed. The results show that the quantitative diagnosis of the hole-edge crack based on the heterogeneous fusion of the guided wave and FBG is more accurate and reliable than that based on the single feature of guided wave. [ABSTRACT FROM AUTHOR]

• It is proved that the elastic wave field excited by the leakage source in the wall is related to the leakage position. • Not limited to a particular moment (like AE), this method can locate the leak source at any time after the leak occurs. • This method simplifies the original multi-sensor array into one and reduces the complexity of real-time leak location system. To satisfy the requirement of real-time monitoring and locating of the leakage in large pressure vessels, a method of leakage source location based on improved convolutional neural network (CNN) is proposed in this paper. In this method, the polar coordinate model of elastic wave propagation excited by continues leakage is established, and the improved CNN is constructed according to its spatial field distribution, and the training set of the network is obtained by finite element simulation. The experimental results show that this method can be applied to locate the leak at any time after it occurs, and only a single sensor array is used. In the experiment of 1 m2 stiffened test plate, the absolute distance error was less than 0.1 m and the absolute angle error was less than 0.1 rad in 90.2 % of all samples. [ABSTRACT FROM AUTHOR]