Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network

Data-driven fault diagnosis is essential for the reliability and safety of industry equipment. However, the lack of real labeled fault data make the machine learning-based diagnosis methods difficult to carry out. To solve this problem, this article proposes a new fault diagnosis framework called multilabel one-dimensional (1-D) generation adversarial network (ML1-D-GAN). In our method, Auxiliary Classifier GAN is utilized first for real damage data generation. Then the generated and real damage data are both used to train the fault classifier. Experimental results reveal that the generated data is applicable, and ML1-D-GAN improves the diagnosing accuracy for real bearing faults from 95% to 98% when trained with the generated data. The scalability of the learning model is also proven in the experiment.

[1]  Wenjing Jin,et al.  Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment , 2016, IEEE Transactions on Industrial Electronics.

[2]  Meikang Qiu,et al.  Senior2Local: A Machine Learning Based Intrusion Detection Method for VANETs , 2018, SmartCom.

[3]  Haidong Shao,et al.  A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders , 2018 .

[4]  Dongxiang Jiang,et al.  Fault diagnosis of wind turbine based on Long Short-term memory networks , 2019, Renewable Energy.

[5]  He Zheng-jia,et al.  Advances in applications of hybrid intelligent fault diagnosis and prognosis technique , 2011 .

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

[7]  Kan Chen,et al.  AMC: Attention Guided Multi-modal Correlation Learning for Image Search , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[9]  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.

[10]  Meng Zhang,et al.  Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition , 2018, ArXiv.

[11]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

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

[13]  Scott R. Granter,et al.  AlphaGo, Deep Learning, and the Future of the Human Microscopist. , 2017, Archives of pathology & laboratory medicine.

[14]  Biao Wang,et al.  LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification , 2018, IEEE Transactions on Industrial Electronics.

[15]  Geoffrey E. Hinton,et al.  Learning a better representation of speech soundwaves using restricted boltzmann machines , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[17]  Jonathon Shlens,et al.  Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.

[18]  Keke Gai,et al.  Optimal resource allocation using reinforcement learning for IoT content-centric services , 2018, Appl. Soft Comput..

[19]  Bin Li,et al.  Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification , 2019, IEEE Transactions on Industrial Electronics.

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

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

[22]  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.

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

[24]  Meikang Qiu,et al.  Reinforcement Learning for Cyber-Physical Systems , 2019, 2019 IEEE International Conference on Industrial Internet (ICII).

[25]  Mohammad Modarres,et al.  Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings , 2017 .

[26]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition , 2012 .

[27]  Wenpeng Yin,et al.  Comparative Study of CNN and RNN for Natural Language Processing , 2017, ArXiv.

[28]  Haidong Shao,et al.  Rolling bearing fault diagnosis using an optimization deep belief network , 2015 .

[29]  Geoffrey E. Hinton,et al.  Unsupervised Learning of Image Transformations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[31]  Jun Jo,et al.  Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection , 2017, 2017 IEEE International Conference on Big Data (Big Data).