Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

Abstract Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating machines, balanced training data for different machine health conditions are assumed in most studies. However, the signals in machine faulty states are usually difficult and expensive to collect, resulting in imbalanced training dataset in most cases. That significantly deteriorates the effectiveness of the existing data-driven approaches. This paper proposes a deep learning-based fault diagnosis method to address the imbalanced data problem by explicitly creating additional training data. Generative adversarial networks are firstly used to learn the mapping between the distributions of noise and real machinery temporal vibration data, and additional realistic fake samples can be generated to balance and further expand the available dataset afterwards. Through experiments on two rotating machinery datasets, it is validated that the data-driven methods can significantly benefit from the data augmentation, and the proposed method offers a promising tool on fault diagnosis with imbalanced training data.

[1]  Steven X. Ding.  Data-Driven Design of Observer-Based Fault Diagnosis Systems , 2014 .

[2]  Chen Lu,et al.  Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification , 2017, Signal Process..

[3]  Yaguo Lei,et al.  Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization , 2018, Mechanical Systems and Signal Processing.

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

[5]  Xiang Li,et al.  Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places , 2020, IEEE Transactions on Industrial Electronics.

[6]  Changqing Shen,et al.  Initial center frequency-guided VMD for fault diagnosis of rotating machines , 2018, Journal of Sound and Vibration.

[7]  Xiang Li,et al.  Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks , 2019, IEEE Transactions on Industrial Electronics.

[8]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[9]  Daniel Morinigo-Sotelo,et al.  Early Fault Detection in Induction Motors Using AdaBoost With Imbalanced Small Data and Optimized Sampling , 2017, IEEE Transactions on Industry Applications.

[10]  Wentao Mao,et al.  Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine , 2017 .

[11]  Diego Cabrera,et al.  Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis , 2015, Neurocomputing.

[12]  Yitao Liang,et al.  A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM , 2015 .

[13]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[14]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[15]  Hui Ma,et al.  Review on dynamics of cracked gear systems , 2015 .

[16]  Bin Yang,et al.  An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings , 2019, Mechanical Systems and Signal Processing.

[17]  Feng Jia,et al.  An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.

[18]  Fernando Nogueira,et al.  Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning , 2016, J. Mach. Learn. Res..

[19]  Xiang Li,et al.  Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning , 2020, IEEE Transactions on Industrial Informatics.

[20]  Kun Yu,et al.  A Combined Polynomial Chirplet Transform and Synchroextracting Technique for Analyzing Nonstationary Signals of Rotating Machinery , 2020, IEEE Transactions on Instrumentation and Measurement.

[21]  Ruqiang Yan,et al.  A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .

[22]  Changqing Shen,et al.  A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines , 2019, Mechanical Systems and Signal Processing.

[23]  Jun Wang,et al.  An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder , 2018, Eng. Appl. Artif. Intell..

[24]  Yaguo Lei,et al.  Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data , 2019, IEEE Transactions on Industrial Electronics.

[25]  Jun Wang,et al.  An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition , 2018, Neurocomputing.

[26]  Diego Cabrera,et al.  Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery , 2019, IEEE Access.

[27]  Zhong Luo,et al.  Research on vibration performance of the nonlinear combined support-flexible rotor system , 2019, Nonlinear Dynamics.

[28]  Lihui Wang,et al.  Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning , 2018, Journal of Manufacturing Systems.

[29]  Mengchen Shan,et al.  A new wind turbine health condition monitoring method based on VMD-MPE and feature-based transfer learning , 2019 .

[30]  Liang Guo,et al.  A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.

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

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

[33]  Maryam Farajzadeh-Zanjani,et al.  A Semi-Supervised Diagnostic Framework Based on the Surface Estimation of Faulty Distributions , 2019, IEEE Transactions on Industrial Informatics.

[34]  Jong-Myon Kim,et al.  Towards bearing health prognosis using generative adversarial networks: Modeling bearing degradation , 2018, 2018 International Conference on Advancements in Computational Sciences (ICACS).

[35]  Wei Zhang,et al.  Multi-Layer domain adaptation method for rolling bearing fault diagnosis , 2019, Signal Process..

[36]  Wentao Mao,et al.  Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study , 2019, IEEE Access.