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Wheelset bearing fault diagnosis is essential to guarantee safe rail system operation. However, real-time monitoring signals of high-speed trains are nonstationary under variable conditions. Additionally, wheelset bearings operate in a normal state most of the time, and therefore, only normal data are available for training in practice. Under such a scenario of variable operating conditions and lacking labelled data, it is still necessary to timely detect the possible abnormal states of wheelset bearings. Currently, one-class classification is a suitable approach for real-time anomaly detection, but its accuracy is affected by the linear relationship of network nodes. Concerning this issue, this paper presents a novel method named scale-independent shrinkage broad learning to detect the abnormal states of wheelset bearings in real time. First, scaled rotation regularization is presented to convert the output constraint into the node distance constraint. Then, the variance-guided mapping mechanism is constructed to solve the linear relationship between nodes. Meanwhile, the optimal network mapping parameters are generated iteratively according to a so-called high-polymerization strategy, with which new output weights are formed after the feedback calculation. This method eliminates the linear correlation of node features during the iteration and realizes one-class classification detection of abnormal states. Finally, wheelset bearing data collected from a test rig of a Chinese rolling stock company are utilized to demonstrate the feasibility and effectiveness of the proposed approach in variable conditions. [ABSTRACT FROM AUTHOR]

Rotating machineries often work under severe and variable operation conditions, which brings challenges to fault diagnosis. To deal with this challenge, this paper discusses the concept of adaptive diagnosis, which means to diagnose faults under variable operation conditions with self-adaptively and little prior knowledge or human intervention. To this end, a novel algorithm is proposed, information geometrical extreme learning machine with kernel (IG-KELM). From the perspective of information geometry, the structure and Riemannian metric of Kernel-ELM is specified. Based on the geometrical structure, an IG-based conformal transformation is created to improve the generalization ability and self-adaptability of KELM. The proposed IG-KELM, in conjunction with variation mode decomposition (VMD) and singular value decomposition (SVD) is utilized for adaptive diagnosis: (1) VMD, as a new self-adaptive signal processing algorithm is used to decompose the raw signals into several intrinsic mode functions (IMFs). (2) SVD is used to extract the intrinsic characteristics from the matrix constructed with IMFs. (3) IG-KELM is used to diagnose faults under variable conditions self-adaptively with no requirement of prior knowledge or human intervention. Finally, the proposed method was applied on fault diagnosis of a bearing and hydraulic pump. The results show that the proposed method outperforms the conventional method by up to 7.25% and 7.78% respectively, in percentages of accuracy. [ABSTRACT FROM AUTHOR]

Accurately diagnosing the faults of wheelset bearings in high-speed trains is critical for ensuring the safety of train operation. In recent years, deep learning methods have made significant progress in this area. However, collecting enough labeled data in high-speed operation would be highly impracticable. In addition, the harsh operating environment of wheelset bearings, coupled with fluctuations in loads due to passenger flow, weather conditions, and other factors, makes it challenging to identify the fault signatures submerged in background noise. To address these issues, this paper proposes a semi-supervised fault diagnosis method based on autocorrelation and an improved flow Gaussian mixture model. First, the autocorrelations of raw bearing vibration signals are calculated to reduce the impact of noise while highlighting the discriminative fault signatures. Second, an improved flow Gaussian mixture model is established, preserving its simplicity and interpretability, to handle the constraints of bearing fault diagnosis with limited labeled samples under variable loads. Finally, the proposed method is tested by using a bearing dataset collected from an industrial railway wheelset bearing test rig. The results demonstrate that the proposed method outperforms the five state-of-the-art methods in terms of diagnostic accuracy and robustness. • A semi-supervised method is proposed for high-speed train wheelset bearing fault diagnosis with limited labeled samples under variable loads. • Autocorrelation is used to pre-process the raw vibration signals while providing an input to the model. • An improved flow Gaussian mixture model with a simple structure and interpretability is used to diagnose the bearing faults under variable loads. • The effectiveness of the method is verified on a railway wheelset bearing dataset. [ABSTRACT FROM AUTHOR]

• A new method based on time-frequency domain combined with phase space reconstruction and singular value decomposition is proposed. • The proposed method integrated with wavelet transform, phase space reconstruction and singular value decomposition can lead to fast and efficient results. • Validity of the proposed method proposed is verified by pratical fault diagnosis application of wind turbine. Operation of wind turbines under fault state will directly affect the power output efficiency of wind farms. This paper proposes a new automatic fault diagnosis method for wind turbines. A fault diagnosis system framework is constructed and data of vibration status of wind turbines collected is processed and used for fault diagnosis. Firstly, wavelet coefficients are obtained using a discrete wavelet transform (DWT) for vibration acceleration signals collected from wind turbines. Then, the wavelet coefficients are sequentially subjected to phase space reconstruction (PSR) and singular value decomposition (SVD) to extract the fault features. Finally, an extreme learning machine (ELM) is used to classify the faults. Experimental results show that the proposed method is more effective and accurate than other fault diagnosis methods for wind turbines, such as support vector machine (SVM) and multiscale convolutional neural network (MSCNN). Image, graphical abstract [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis and continual learning techniques for train transmission systems are becoming more appealing to ensure the operation safety and reliability of trains. However, existing related methods have the following limitations. 1) They build separate fault diagnosis networks for each key component in train transmission systems, which not only brings burdensome work for training and managing diagnosis networks but also ignores the influence of fault propagation and interaction between adjacent components. 2) They rely on enough class-incremental samples for continual learning to obtain the growth diagnosis ability, but the accumulations of incremental fault samples are long-period processes, leading to belated network evolution and hysteretic performance enhancement. To overcome the above-mentioned limitations, an evolvable system-level fault diagnosis framework is proposed for train transmission systems. In the proposed framework, a novel graph neural network is first constructed based on component spatial relationships, which is able to effectively learn and capture the interaction between components and integrate fault diagnosis tasks into a unified framework. Then, an anhysteretic evolution learning mechanism is developed to avoid lengthy waiting for new fault sample accumulations, in which over-fitting due to insufficient incremental fault sample is addressed by employing prototype-based classifiers and diagnostic knowledge is transferred and captured by maintaining and generating prototypes. The effectiveness of the proposed diagnosis framework is verified by taking a case study of fault diagnosis for subway train transmission systems, and its superiority is demonstrated by comparison with some stage-of-the-art diagnosis methods. [ABSTRACT FROM AUTHOR]

• A novel combined method for bogie diagnosis is proposed. • A novel loss function called Imbalance-weighted cross-entropy is proposed to deal with data imbalance. • The proposed method is robust to noise and different operating conditions and imbalanced data. The condition monitoring of bogies plays an important role in the safe operation of rail transit systems. In practice, it is difficult to diagnose bogies under variable operating conditions for traditional methods. In this paper, deep residual learning is used to deal with this problem by utilizing its powerful feature self-learning characteristics and fast kurtogram is used to further enhance the robustness of the method to variable operating conditions and noises. Besides, considering the performance of data-driven deep learning method is easily to be affected by data imbalance which often occurs in practical data collection, a novel loss function called imbalance-weighted cross-entropy (IWCE) is proposed in this paper for networks training to solve the data imbalance problem. Finally, the robustness to variable operating conditions, noises and data imbalance of the proposed method is verified by two case studies. [ABSTRACT FROM AUTHOR]

Bearings are vital components in industrial machines. Diagnosing the fault of rolling element bearings and ensuring normal operation is essential. However, the faults of rolling element bearings under variable conditions and the adaptive feature selection has rarely been discussed until now. Thus, it is essential to develop a practicable method to put forward the disposal of the fault under variable conditions. Considering these issues, this paper uses the method based on the Mahalanobis Taguchi System (MTS), and overcomes two shortcomings of MTS: (1) MTS is an effective tool to classify faults and has strong robustness to operating conditions, but it can only handle binary classification problems, and this paper constructs the multiclass measurement scale to deal with multi-classification problems. (2) MTS can determine important features, but uses the hard threshold to select the features, and this paper selects the proper feature sequence instead of the threshold to overcome the lesser adaptivity of the threshold configuration for signal-to-noise gain. Hence, this method proposes a novel method named adaptive Multiclass Mahalanobis Taguchi system (aMMTS), in conjunction with variational mode decomposition (VMD) and singular value decomposition (SVD), and is employed to diagnose the faults under the variable conditions. Finally, this method is verified by using the signal data collected from Case Western Reserve University Bearing Data Center. The result shows that it is accurate for bearings fault diagnosis under variable conditions. [ABSTRACT FROM AUTHOR]

Nowadays, with the growing scale of high-speed railways and the increasing number of high-speed trains, the research on train equipment fault diagnosis and health management becomes more and more significant. Bearings are parts which are prone to be the failure equipment on high-speed trains. The temperature of a faulty bearing will increase suddenly during the working process, which may lead to potential accidents. So the axle temperature prediction has become a key research direction. This paper proposes a new organization form of axle temperature data, which connects axle temperature measurement points according to their locations so as to form a graph. Then, based on the Graph Convolutional Network (GCN) and Gated Recurrent Units (GRU) models, a new framework named GCG which combines the GCN and GRU is proposed to extract features and predict axle temperature. Finally, the experiments are conducted based on actual data. The results show that the prediction accuracy and tracking sensitivity are better than other advanced methods. [ABSTRACT FROM AUTHOR]

Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency. Although proved to be a powerful method in detecting the resonance band excited by faults, the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise. To well process the bearing fault signal in the presence of impulsive noise, this paper proposes a fault diagnosis method based on the cyclic correntropy (CCE) function and its spectrum. Furthermore, an important parameter of CCE function, namely kernel size, is analyzed to emphasize its critical influence on the fault diagnosis performance. Finally, comparisons with the SK-based Fast Kurtogram are conducted to highlight the superiority of the proposed method. The experimental results show that the proposed method not only largely suppresses the impulsive noise, but also has a robust self-adaptation ability. The application of the proposed method is validated on a simulated signal and real data, including rolling element bearing data of a train axle. [ABSTRACT FROM AUTHOR]