Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Abstract

Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years. However, many deep learning methods cannot fully extract fault information to recognize mechanical health states when processing high-dimensional samples. Therefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNN) is proposed in this study to accomplish fault recognition of high-dimensional samples. First, several 1D DCNN models with different activation functions are trained through dimension reduction learning to obtain different fault features from high-dimensional samples. Second, the obtained features are constructed into 2D images with multiple channels through a conversion method. The integrated 2D feature images can effectively represent the fault characteristic contained in raw high-dimension vibration signals. Lastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features from the 2D images and identify the fault mode of the mechanical equipment by adopting a softmax classifier. The proposed method, which is validated using the bearing public dataset of Case Western Reserve University, USA and a one-stage reduction gearbox dataset, has high recognition accuracy. Compared with other classical deep learning methods, the proposed fault diagnosis method has considerable improvements.

Graphical abstract

Keywords

fault intelligent diagnosis / deep learning / deep convolutional neural network / high-dimensional samples

References

[1] Liu R, Yang B, Zio E, . Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 2018, 108: 33–47

[2] Zhang H. Fault diagnosis and life prediction of mechanical equipment based on artificial intelligence. Journal of Intelligent & Fuzzy Systems, 2018, 37(3): 3535–3544

[3] Jia F, Lei Y, Guo L, . A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines. Neurocomputing, 2018, 272: 619–628

[4] Liu J, Hu Y, Wang Y, . An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis. Measurement Science & Technology, 2018, 29(5): 055103

[5] Azamfar M, Li X, Lee J. Intelligent ball screw fault diagnosis using a deep domain adaptation methodology. Mechanism and Machine Theory, 2020, 151: 103932

[6] Ainapure A, Li X, Singh J, . Enhancing intelligent cross-domain fault diagnosis performance on rotating machines with noisy health labels. Procedia Manufacturing, 2020, 48: 940–946

[7] Zhang X, Guo S, Jiang L. Semi-supervised fault identification based on improved Laplace feature mapping and constraint seed K-means. Journal of Vibration and Shock, 2019, 38(16): 93–99 (in Chinese)

[8] Widodo A, Yang B S. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 2007, 21(6): 2560–2574

[9] Sun Y, Zhang S, Miao C, . Improved BP neural network for transformer fault diagnosis. Journal of China University of Mining and Technology, 2007, 17(1): 138–142

[10] Liu J, Hu Y, Wu B, . A hybrid generalized hidden markov model-based condition monitoring approach for rolling bearings. Sensors (Basel), 2017, 17(5): 1143

[11] Hoang D T, Kang H J. A survey on deep learning based bearing fault diagnosis. Neurocomputing, 2019, 335: 327–335

[12] Li X, Li J, Qu Y, . Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning. Chinese Journal of Aeronautics, 2020, 33(2): 418–426

[13] Zhang W, Li C, Peng G, . A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing, 2018, 100: 439–453

[14] Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks, 2015, 61: 85–117

[15] Tang S, Yuan S, Zhu Y. Deep learning-based intelligent fault diagnosis methods toward rotating machinery. IEEE Access: Practical Innovations, Open Solutions, 2020, 8: 9335–9346

[16] Jing L, Wang T, Zhao M, . An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox. Sensors (Basel), 2017, 17(2): 414

[17] Zhu J, Hu T, Jiang B, . Intelligent bearing fault diagnosis using PCA-DBN framework. Neural Computing & Applications, 2020, 32(14): 10773–10781

[18] Lecun Y, Bottou L, Bengio Y, . Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278–2324

[19] Zhao D, Wang T, Chu F. Deep convolutional neural network based planet bearing fault classification. Computers in Industry, 2019, 107: 59–66

[20] Chen H, Hu N, Cheng Z, . A deep convolutional neural network based fusion method of two-direction vibration signal data for health state identification of planetary gearboxes. Measurement, 2019, 146: 268–278

[21] Wu C, Jiang P, Ding C, . Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network. Computers in Industry, 2019, 108: 53–61

[22] Kumar A, Zhou Y, Gandhi C P, . Bearing defect size assessment using wavelet transform based deep convolutional neural network (DCNN). Alexandria Engineering Journal, 2020, 59(2): 999–1012

[23] Li Y, Du X, Wan F, . Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging. Chinese Journal of Aeronautics, 2020, 33(2): 427–438

[24] Wen L, Li X, Gao L, . A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5990–5998

[25] Xue Y, Dou D, Yang J. Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine. Measurement, 2020, 156: 107571

[26] Hu Z X, Wang Y, Ge M F, . Data-driven fault diagnosis method based on compressed sensing and improved multiscale network. IEEE Transactions on Industrial Electronics, 2020, 67(4): 3216–3225

[27] Li H, Huang J, Ji S. Bearing fault diagnosis with a feature fusion method based on an ensemble convolutional neural network and deep neural network. Sensors (Basel), 2019, 19(9): 2034

[28] Zhou Q, Li Y, Tian Y, . A novel method based on nonlinear auto-regression neural network and convolutional neural network for imbalanced fault diagnosis of rotating machinery. Measurement, 2020, 161: 107880

[29] Pezzotti N, Lelieveldt B, Maaten L, . Approximated and user steerable tSNE for progressive visual analytics. IEEE Transactions on Visualization and Computer Graphics, 2017, 23(7): 1739–1752

[30] Smith W A, Randall R B. Rolling element bearing diagnostics using the case western reserve university data: A benchmark study. Mechanical Systems and Signal Processing, 2015, 64–65: 100–131

[31] Srivastava N, Hinton G, Krizhevsky A, . Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15(1): 1929–1958

[32] Agostini C E, Sampaio M A. Probabilistic neural network with bayesian-based, spectral torque imaging and deep convolutional autoencoder for PDC bit wear monitoring. Journal of Petroleum Science Engineering, 2020, 193: 107434

[33] Jia F, Lei Y, Lin J, . Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 2016, 72–73: 303–315

[34] Zhang X, Guo S, Li Y, . Semi-supervised fault identification based on laplacian eigenmap and deep belief networks. Journal of Mechanical Engineering, 2019, 56(1): 69

[35] Gong W, Chen H, Zhang Z, . A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion. Sensors (Basel), 2019, 19(7): 1693

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