ZHANG Jing, TIAN Jing, WEN Tao, et al., “Deep Fault Diagnosis for Rotating Machinery with Scarce Labeled Samples,” Chinese Journal of Electronics, vol. 29, no. 4, pp. 693-704, 2020, doi: 10.1049/cje.2020.05.016
Citation: ZHANG Jing, TIAN Jing, WEN Tao, et al., “Deep Fault Diagnosis for Rotating Machinery with Scarce Labeled Samples,” Chinese Journal of Electronics, vol. 29, no. 4, pp. 693-704, 2020, doi: 10.1049/cje.2020.05.016

Deep Fault Diagnosis for Rotating Machinery with Scarce Labeled Samples

doi: 10.1049/cje.2020.05.016
Funds:  This work is supported by the National Key Research and Development Program (No.2016YFE0200900), the National Natural Science Foundation of China (No.61806064, No.61806062, No.61751304, No.61873077), and Open Foundation of Key Laboratory of Advanced Public Transportation Science, Ministry of Transport, PRC.
  • Received Date: 2019-02-27
  • Rev Recd Date: 2019-05-16
  • Publish Date: 2020-07-10
  • Early and accurately detecting faults is crucial for the modern manufacturing system. We proposed a novel Deep fault diagnosis (DFD) method for rotating machinery with scarce labeled samples. A spectrogram of the raw vibration signal is calculated by applying a Short-time Fourier transform (STFT). Several candidate Support vector machine (SVM) models are trained with different combinations of features in the feature pool with scarce labeled samples. By evaluating the pretrained SVM models on the validation set, the most discriminative features and best-performed SVM models can be selected, which are used to make predictions on the unlabeled samples. The predicted labels reserve the expert knowledge originally carried by the SVM model. They are combined together with the scarce fine labeled samples to form an Augmented training set (ATS). Finally, a novel 2D deep Convolutional neural network (CNN) model is trained on the ATS to learn more discriminative features and a better classifier. Experimental results on two fault diagnosis datasets demonstrate the effectiveness of the proposed DFD.
  • loading
  • W.J. Sun, S.Y. Shao, R. Zhao, et al., “A sparse auto encoderbased deep neural network approach for induction motor faults classification”, Measurement, Vol.89, pp.171-178, 2016.
    T. Ince, S. Kiranyaz, L. Eren, et al., “Real-time motor fault detection by 1D convolutional neural networks”, IEEE Transactions on Industrial Electronics, Vol.63, No.11, pp.7067-7075, 2016.
    M. Gan, C. Wang and C.A. Zhu, “Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings”, Mechanical Systems and Signal Processing, Vol.72-73, pp.92-104, 2016.
    D.Z. Li, W. Wang and F. Ismailm, “An enhanced bispectrum technique with auxiliary frequency injection for induction motor health condition monitoring”, IEEE Transactions on Instrumentation & Measurement, Vol.64, No.10, pp.2679-2687, 2015.
    S. Dash and V. Venkatasubramanian, “Challenges in the industrial applications of fault diagnostic systems”, Computers and Chemical Engineering, Vol.24, No.2-7, pp.785-791, 2000.
    A. Widodo and B.S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis”, Mechanical Systems and Signal Processing, Vol.21, No.6, pp.2560-2574, 2007.
    Y.G. Lei, Z.J. He and Y.Y. Zi, “A new approach to intelligent fault diagnosis of rotating machinery”, Expert Systems with Applications, Vol.35, No.4, pp.1593-1600, 2008.
    J. Zhang, D.Q. Zhang, M.Y. Yang, et al., “Fault diagnosis for rotating machinery with scarce labeled samples: A Deep CNN method based on knowledge-transferring from shallow models”, International Conference on Control, Automation and Information Sciences, Hangzhou, China, pp.482-487, 2018.
    W. Zhou, T.G. Habetler and R.G. Harley, “ Bearing fault detection via stator current noise cancellation and statistical control”, IEEE Transactions on Industrial Electronics, Vol.55, No.12, pp.4260-4269, 2008.
    B. Sreejith, A.K. Verma and A. Srividya, “ Fault diagnosis of rolling element bearing using time-domain features and neural networks”, IEEE Region 10 and the Third International Conference on Industrial and Information Systems, Peradeniya, Sri Lanka, pp.1-6, 2009.
    Y. Liu, J.H. Zhang, K.J. Qin, et al., “Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine”, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol.232, No.5, pp.881-894, 2018.
    P.K. Wong, Z.X. Yang, C.M Vong, et al., “Real-time fault diagnosis for gas turbine generator systems using extreme learning machin”, Neurocomputing, Vol.128, pp.249-257, 2014.
    Z.Q. Chen, C. Li and R.V. Sanchez, “Gearbox fault identification and classification with convolutional neural networks”, Shock and Vibration, Vol.2015, Article ID 390134, 10 pages, 2015.
    Y. Lv, R. Yuan and G.B. Song, “Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing”, Mechanical Systems and Signal Processing, Vol.81, pp.219-234, 2016.
    J.D. Zheng, H.Y. Pan, X.L Qi, et al., “Enhanced empirical wavelet transform based time-frequency analysis and its application to Rolling Bearing Fault Diagnosis”, Acta Electronica Sinica, Vol.46, No.2, pp.358-364, 2018.(in Chinese)
    C. Li, V. Sanchez, G. Zurita, et al., “Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time-frequency ridge enhancement”, Isa Transactions, Vol.60, pp.274-284, 2016.
    Y. Tian, J. Ma, C. Lu, et al., “Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine”, Mechanism and Machine Theory, Vol.90, pp.175-186, 2015.
    M.A. Hearst, S.T. Dumais, E. Osman, et al., “Support vector machines”, IEEE Intelligent Systems, Vol.13, No.4, pp.18-28, 1998.
    X.Y. Zhang, Y.T. Liang, J.Z. Zhou, et al., “A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM”, Measurement, Vol.69, pp.164-179, 2015.
    A.N. Wang, M. Sha, L.M. Liu, et al., “A new process industry fault diagnosis algorithm based on ensemble improved binarytree SVM”, Chinese Journal of Electronics, Vol.24, No.2, pp.258-262, 2015.
    Y. Freund and R.E. Robert, “A decision-theoretic generalization of on-line learning and an application to boosting”, Journal of computer and system sciences, Vol.55, No.1, pp.119-139, 1997.
    H.W. Liu, L. Liu and H.J. Zhang, “Boosting feature selection using information metric for classification”, Neurocomputing, Vol.73, No.1-3, pp.295-303, 2009.
    G.B. Huang, Q.Y. Zhu and C.K. Siew, “Extreme learning machine: Theory and applications”, Neurocomputing, Vol.70, No.1-3, pp.489-501, 2006.
    G.E. Hinton, S. Osindero and Y.W. Teh, “A fast learning algorithm for deep belief nets”, Neural computation, Vol.18, No.7, pp.1527-1554, 2006.
    H.D. Shao, H.K. Jiang, X. Zhang, et al., “Rolling bearing fault diagnosis using an optimization deep belief network”, Measurement Science and Technology, Vol.26, No.11, 2015.
    Y. Lecun and Y. Bengio, “Convolutional networks for images, speech, and time series”, The handbook of brain theory and neural networks, 1995.
    F. Gao, M. Wang, J. Wang, et al., “A novel separability objective function in CNN for feature extraction of SAR images”, Chinese Journal of Electronics, Vol.28, No.2, pp.423-429, 2019.
    D.D. Bai, C.Q. Wang, B. Zhang, et al., “CNN feature boosted seqSLAM for real-Time loop closure detection”, Chinese Journal of Electronics, Vol.27, No.3, pp.488-499, 2018.
    J.Y. Gan, Y.K. Zhai, Y. Huang, et al., “Research of facial beauty prediction based on deep convolutional features using double activation layer”, Acta Electronica Sinica, Vol.47, No.3, pp.636-642, 2019.(in Chinese)
    L. Deng, J.Y Li J.T. Huang, et al., “Recent advances in deep learning for speech research at Microsoft”, IEEE International Conference on Acoustics, Vancouver, British Columbia, Canada, pp.8604-8608, 2013.
    A. Krizhevsky, I. Sutskever and G.E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, International Conference on Neural Information Processing Systems, Doha, Qatar, pp.1097-1105, 2012.
    S.Q. Ren, K.M. He, R. Girshick, et al., “Faster R-CNN: towards real-time object detection with region proposal networks”, International Conference on Neural Information Processing Systems, Kuching, Malaysia, pp.91-99, 2015.
    F. Jia, Y.G. Lei, J. Lin, et al., “Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data”, Mechanical Systems and Signal Processing, Vol.72-73, pp.303-315, 2016.
    M. Xia, T. Li, L. Xu, et al., “Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks”, IEEE/ASME Transactions on Mechatronics, Vol.23, No.1, pp.101-110, 2017.
    J.H. Sun, Z.W. Xiao and Y.X. Xie, “Automatic multi-fault recognition in TFDS based on convolutional neural network”, Neurocomputing, Vol.222, pp.127-136, 2017.
    M. Meng, Y.J. Chua, E. Wouterson, et al., “Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks”, Neurocomputing, Vol.257, pp.128-135, 2017.
    L. Deng, M.L. Seltzer, D. Yu, et al., “Binary coding of speech spectrograms using a deep auto-encoder”, 11th Annual Conference of the International Speech Communication Association, Makuhari, Japan, 2010.
    J.M. Zhang, Q.Q. Huang, H.L. Wu, et al., “A shallow network with combined pooling for fast traffic sign recognition”, Information, Vol.8, No.2, pp.45, 2017.
    S. Santurkar, D. Tsipras, A. Ilyas, et al., “How does batch normalization help optimization?”, Advances in Neural Information Processing Systems, Montreal, Canada, pp.2483-2493, 2018.
    Y.Q. Jia, E. Shelhamer, J. Donahue, et al., “Caffe: Convolutional architecture for fast feature embedding”, Proc.of the 22nd ACM international conference on Multimedia, Orlando, Florida USA, pp.675-678, 2014.
    L.V.D. Maaten and G. Hinton, “Visualizing data using tSNE”, Journal of machine learning research, Vol.9, No.Nov, pp.2579-2605, 2008.
    F.N. Zhou, P. Hu, S. Yang, et al., “A multimodal feature fusion-based deep learning method for online fault diagnosis of rotating machinery”, Sensors, Vol.18, No.10, pp.3521, 2018.
    H.Y. Pan, J.D. Zheng, Y. Yang, et al., “Research on combined intelligent fault diagnostic method based on CELCD and MFVPMCD”, Acta Electronica Sinica, Vol.45, No.3, pp.546-551, 2017. (in Chinese)
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (569) PDF downloads(122) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return