Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks

Mechanical fault datasets are always highly imbalanced with abundant common mechanical fault samples but a paucity of samples from rare fault conditions. To overcome this weakness, the simulation of rare fault signals is proposed in this paper. Specifically, frequency spectra are employed as model signals, then Wasserstein generative adversarial network (WGAN) is implemented to generate simulated signals based on a labeled dataset. Finally, the real and artificial signals are combined to train stacked autoencoders (SAE) to detect mechanical health conditions. To validate the effectiveness of the proposed WGAN-SAE method, two specially designed experiments are carried out and some traditional methods are adopted for comparison. The diagnosis results show that the proposed method can deal with imbalanced fault classification problem much more effectively. The improved performance is mainly due to the artificial fault signals generated from the WGAN to balance the dataset, where the signals that are lacking in training dataset are effectively augmented. Furthermore, the learned features in each layer of the generator network are also analyzed via visualization, which may help us understand the working process of the WGAN.

[1]  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.

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

[3]  Bo Jin,et al.  Sequential Fault Diagnosis Based on LSTM Neural Network , 2018, IEEE Access.

[4]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[5]  Stefan Scherer,et al.  Learning representations of emotional speech with deep convolutional generative adversarial networks , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Shahryar Rahnamayan,et al.  Customer shopping pattern prediction: A recurrent neural network approach , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[7]  Shahrokh Valaee,et al.  Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks , 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  Yoshua Bengio,et al.  Scaling learning algorithms towards AI , 2007 .

[9]  Weiguo Huang,et al.  Time-Frequency Squeezing and Generalized Demodulation Combined for Variable Speed Bearing Fault Diagnosis , 2019, IEEE Transactions on Instrumentation and Measurement.

[10]  Max A. Viergever,et al.  Generative Adversarial Networks for Noise Reduction in Low-Dose CT , 2017, IEEE Transactions on Medical Imaging.

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

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

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

[14]  Léon Bottou,et al.  Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.

[15]  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.

[16]  Yaguo Lei,et al.  Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery , 2016 .

[17]  Yanqing Zhang,et al.  SVMs Modeling for Highly Imbalanced Classification , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Changqing Shen,et al.  Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery , 2017, IEEE Access.

[19]  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.

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

[21]  Hojjat Salehinejad,et al.  Recurrent Neural Networks for Sequential Phenotype Prediction in Genomics , 2015, 2015 International Conference on Developments of E-Systems Engineering (DeSE).

[22]  C. Villani.  Optimal Transport: Old and New , 2008 .

[23]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

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

[25]  Ana Maria Mendonça,et al.  End-to-End Adversarial Retinal Image Synthesis , 2018, IEEE Transactions on Medical Imaging.

[26]  Hasmat Malik,et al.  Proximal Support Vector Machine (PSVM) Based Imbalance Fault Diagnosis of Wind Turbine Using Generator Current Signals , 2016 .

[27]  Wei Jiang,et al.  Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. , 2018, ISA transactions.

[28]  Hirokazu Kameoka,et al.  Generative adversarial network-based postfilter for statistical parametric speech synthesis , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[29]  Yaping Lin,et al.  Synthetic minority oversampling technique for multiclass imbalance problems , 2017, Pattern Recognit..

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