Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis

Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling bearings. However, these neural networks are lack of interpretability for fault diagnosis tasks. That is, how to understand and learn discriminant fault features from complex monitoring signals remains a great challenge. Considering this challenge, this article explores the use of the attention mechanism in fault diagnosis networks and designs attention module by fully considering characteristics of rolling bearing faults to enhance fault-related features and to ignore irrelevant features. Powered by the proposed attention mechanism, a multiattention one-dimensional convolutional neural network (MA1DCNN) is further proposed to diagnose wheelset bearing faults. The MA1DCNN can adaptively recalibrate features of each layer and can enhance the feature learning of fault impulses. Experimental results on the wheelset bearing dataset show that the proposed multiattention mechanism can significantly improve the discriminant feature representation, thus the MA1DCNN outperforms eight state-of-the-arts networks.

[1]  Xiang Li,et al.  Deep residual learning-based fault diagnosis method for rotating machinery. , 2019, ISA transactions.

[2]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[3]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[4]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[5]  Jing Tian,et al.  Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis , 2016, IEEE Transactions on Industrial Electronics.

[6]  Xuefeng Chen,et al.  Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine , 2017, IEEE Transactions on Industrial Informatics.

[7]  P. Tse,et al.  A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing , 2005 .

[8]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Moncef Gabbouj,et al.  Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.

[10]  Ming J. Zuo,et al.  Time-frequency representation based on robust local mean decomposition for multicomponent AM-FM signal analysis , 2017 .

[11]  Haidong Shao,et al.  Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network , 2018, IEEE Transactions on Industrial Electronics.

[12]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[13]  Zhiwei Gao,et al.  From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis , 2013, IEEE Transactions on Industrial Informatics.

[14]  Sergey A. Shevchik,et al.  Prediction of Failure in Lubricated Surfaces Using Acoustic Time–Frequency Features and Random Forest Algorithm , 2017, IEEE Transactions on Industrial Informatics.

[15]  Min Xia,et al.  Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks , 2018, IEEE/ASME Transactions on Mechatronics.

[16]  Deyong You,et al.  Multisensor Fusion System for Monitoring High-Power Disk Laser Welding Using Support Vector Machine , 2014, IEEE Transactions on Industrial Informatics.

[17]  Jun Yan,et al.  Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox , 2019, IEEE Transactions on Industrial Electronics.

[18]  ZhiQiang Chen,et al.  Gearbox Fault Identification and Classification with Convolutional Neural Networks , 2015 .

[19]  Jagath Sri Lal Senanayaka,et al.  Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults , 2019, IEEE Transactions on Industrial Informatics.

[20]  Fei Shen,et al.  Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks , 2018, IEEE Transactions on Industrial Electronics.

[21]  Wei Zhang,et al.  A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals , 2017, Sensors.

[22]  Ruqiang Yan,et al.  Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis , 2017, IEEE Transactions on Industrial Informatics.

[23]  Ruqiang Yan,et al.  Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning , 2019, IEEE Transactions on Industrial Informatics.

[24]  Gaoliang Peng,et al.  A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load , 2018, Mechanical Systems and Signal Processing.

[25]  Ming Zhao,et al.  A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox , 2017 .

[26]  Chuang Sun,et al.  Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory Data , 2018, IEEE Transactions on Industrial Informatics.

[27]  Balbir S. Dhillon,et al.  Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network , 2012 .

[28]  Jawad Faiz,et al.  EMD-Based Analysis of Industrial Induction Motors With Broken Rotor Bars for Identification of Operating Point at Different Supply Modes , 2014, IEEE Transactions on Industrial Informatics.

[29]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[30]  Liang Chen,et al.  Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis , 2016 .

[31]  Robert Babuska,et al.  Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks , 2017, IEEE Transactions on Neural Networks and Learning Systems.