A novel transfer learning method for bearing fault diagnosis under different working conditions

Abstract Transfer learning has attracted great attention in intelligent fault diagnosis of bearings under different working conditions. However, existing studies have the following limitation. (1) The metric of feature distribution discrepancy between different working conditions is not sufficiently domain adaptive. (2) The decision boundaries among different classes are not sufficiently clear in the target domain. To overcome the aforementioned limitations: (1) A fault transfer diagnosis model based on deep convolution Wasserstein adversarial networks(DCWANs) is proposed to handle the first limitation; (2) A variance constraint is developed for the DCWAN-based model to increase the aggregation of extracted features, which enlarges the margins among features of different classes in the source domain and also helps in feature extraction by adaptively aligning features by classes under different working conditions, thus, overcoming the second limitation. Experimental results showed that the proposed model achieves a higher fault diagnosis accuracy than existing models.

[1]  Steven Verstockt,et al.  Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .

[2]  Jia Mingxing,et al.  Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification , 2020 .

[3]  Jinde Zheng,et al.  Rolling bearing fault diagnosis based on partially ensemble empirical mode decomposition and variable predictive model-based class discrimination , 2016 .

[4]  S.A.V. Satya Murty,et al.  Roller element bearing fault diagnosis using singular spectrum analysis , 2013 .

[5]  Shunming Li,et al.  Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method , 2019, Neurocomputing.

[6]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[7]  Ying Zhang,et al.  An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image , 2020, Measurement.

[8]  Zhao Ke,et al.  A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings , 2018, Mechanical Systems and Signal Processing.

[9]  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.

[10]  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.

[11]  Wei Zhang,et al.  ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis , 2018, Neurocomputing.

[12]  Walter Sextro,et al.  Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification , 2016, PHM Society European Conference.

[13]  Takehisa Yairi,et al.  A review on the application of deep learning in system health management , 2018, Mechanical Systems and Signal Processing.

[14]  Yaguo Lei,et al.  Deep Transfer Diagnosis Method for Machinery in Big Data Era , 2019, Journal of Mechanical Engineering.

[15]  Philip S. Yu,et al.  Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.

[16]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

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

[18]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[19]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[20]  Felipe Albertao,et al.  Incremental dictionary learning for fault detection with applications to oil pipeline leakage detection , 2011 .

[21]  Wei Zhang,et al.  A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning , 2018, Neurocomputing.

[22]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[23]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[24]  Yonghong Zhang,et al.  Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network , 2017, Chinese Journal of Mechanical Engineering.

[25]  Sanjay H Upadhyay,et al.  A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings , 2016 .

[26]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[27]  Koby Crammer,et al.  A theory of learning from different domains , 2010, Machine Learning.

[28]  Mengjie Zhang,et al.  Domain Adaptive Neural Networks for Object Recognition , 2014, PRICAI.