A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings

A comprehensive review on convolutional neural network in machine fault diagnosis

A systematic review of deep transfer learning for machinery fault diagnosis

Intelligent fault detection and diagnosis, as an important approach, play a crucial role in ensuring the stable, reliable and safe operation of rolling bearings, which is one of the most important components in the rotating machinery. In real industries, it is common to face that the issues of severe data imbalance and distribution difference since the number of fault data is small and the equipments frequently change the working conditions according to the production. To accurately and automatically identify the conditions of rolling bearings, a normalized convolutional neural network is proposed for the diagnosis of different fault severities and orientations considering data imbalance and variable working conditions. First, the batch normalization is adopted as a novel application to eliminate feature distribution difference, which is the prerequisite for ensuring generalization ability under different working conditions. Then, a special model structure is established and the overall performances of the proposed model are optimized by iterative update, which combines the exponential moving average technology. Finally, the proposed model is applied to the fault diagnosis under different data imbalance cases and working conditions. The effectiveness of the proposed method is verified based on two popular experiment dataset, and the diagnosis performance is widely evaluated in different scenarios. Comparisons with other commonly used methods and related works on the same dataset demonstrate the superiority of the proposed method. The results show that the proposed method has excellent diagnosis accuracy and admirable robustness, and also has sufficient stability on the data imbalance.

Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions

An adaptive deep transfer learning method for bearing fault diagnosis

Traditional methods used for intelligent condition monitoring and diagnosis significantly depend on manual feature extraction and selection. To address this issue, a transfer learning-convolutional neural network (TLCNN) based on AlexNet is proposed for bearing fault diagnosis. Firstly, a 2D image representation method converts vibration signals to 2D time-frequency images. Secondly, the proposed TLCNN model extracts the features of the 2D time-frequency images and achieves the classification conditions of the bearing, which is faster to train and more accurate. Thirdly, t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize the feature learning process to demonstrate the feature learning ability of the proposed model. The experimental results verify that the proposed fault diagnosis model has higher accuracy and has much better robustness against noise than other deep learning and traditional methods.

https://iopscience.iop.org/article/10.1088/1361-6501/ab0793/pdf

A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network

New procedure for gear fault detection and diagnosis using instantaneous angular speed

Fault diagnosis of rolling bearing under fluctuating speed and variable load based on TCO Spectrum and Stacking Auto-encoder

A new strategy of instantaneous angular speed extraction and its application to multistage gearbox fault diagnosis

Recently, Instantaneous Angular Speed (IAS) measurement is successfully established and prevalently applied to a wide variety of machines due to the hypothesis that the speed fluctuation of rotating machinery carries plentiful dynamic responses. Nevertheless, exploration and application based on angular signal are still insufficient. Under the same hypothesis, in this paper, we introduced an extended algorithm named Instantaneous Angular Phase Demodulation (IAPD), together with the selection of optimal sideband family to extract the Instantaneous Angular Displacement Fluctuation (IADF) signal. In order to evaluate the performance of IADF signal, an effective approach was demonstrated using IADF signal to address the fault detection and diagnosis issue. After extracting the IADF signal, a much effective method was developed to deal with the large amount of data generated during the signal collection process. Then, we used the well-developed techniques, i.e., empirical mode decomposition (EMD) and envelope analysis, to undertake the signal de-noising and feature extraction task. The effectiveness and capability of the IADF signal were evaluated by two kinds of gearboxes under differentconditions in practice. In particular, the prevalent IAS signal and vibration signal were also involved and testified by the proposed procedure. Experimental results demonstrated that by means of the IADF signal, the combination of EMD and envelope analysis not only provided accurate identification results with a higher signal-to-noise ratio, but was also capable of revealing the fault characteristics significantly and effectively. In contrast, although the IAS signal had the potential ability to diagnose the serious fault, it failed for the slight crack case. Moreover, the same procedure even its improvements, i.e., ensemble empirical mode decomposition and local mean decomposition, all failed to recognize the faults in terms of vibration signals.

Xining Zhang

Papers

A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network

New procedure for gear fault detection and diagnosis using instantaneous angular speed

Fault diagnosis of rolling bearing under fluctuating speed and variable load based on TCO Spectrum and Stacking Auto-encoder

A new strategy of instantaneous angular speed extraction and its application to multistage gearbox fault diagnosis

Measurement of Instantaneous Angular Displacement Fluctuation and its applications on gearbox fault detection.