Chinese Journal of Mechanical Engineering >

2021 , Vol. 34 >Issue 4: 58 - 58

DOI: https://doi.org/10.1186/s10033-021-00580-5

Intelligent Manufacturing Technology

An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

  • Zhiwu Shang ,
  • Wanxiang Li ,
  • Maosheng Gao ,
  • Xia Liu ,
  • Yan Yu
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  • 1. School of Mechanical Engineering, Tiangong University, Tianjin 300387, China;
    2. Tianjin Modern Electromechanical Equipment Technology Key Laboratory, Tianjin 300387, China

收稿日期: 2020-02-28

  修回日期: 2021-01-25

  网络出版日期: 2021-12-21

基金资助

Supported by National Natural Science Foundation of China and Civil Aviation Administration of China Joint Funded Project (Grant No. U1733108) and Key Project of Tianjin Science and Technology Support Program (Grant No. 16YFZCSY00860)

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An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

  • Zhiwu Shang ,
  • Wanxiang Li ,
  • Maosheng Gao ,
  • Xia Liu ,
  • Yan Yu
Expand
  • 1. School of Mechanical Engineering, Tiangong University, Tianjin 300387, China;
    2. Tianjin Modern Electromechanical Equipment Technology Key Laboratory, Tianjin 300387, China

Received date: 2020-02-28

  Revised date: 2021-01-25

  Online published: 2021-12-21

Supported by

Supported by National Natural Science Foundation of China and Civil Aviation Administration of China Joint Funded Project (Grant No. U1733108) and Key Project of Tianjin Science and Technology Support Program (Grant No. 16YFZCSY00860)

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摘要

For a single-structure deep learning fault diagnosis model, its disadvantages are an insufficient feature extraction and weak fault classification capability. This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy. First, a normal autoencoder, denoising autoencoder, sparse autoencoder, and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure. A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features. Finally, the advantage of the deep belief network probability model is used as the fault classifier to identify the faults. The effectiveness of the proposed method was verified by a gearbox test-bed. Experimental results show that, compared with traditional and existing intelligent fault diagnosis methods, the proposed method can obtain representative information and features from the raw data with higher classification accuracy.

关键词： Fault diagnosis; Feature fusion; Information entropy; Deep autoencoder; Deep belief network

本文引用格式

Zhiwu Shang , Wanxiang Li , Maosheng Gao , Xia Liu , Yan Yu . An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy[J]. Chinese Journal of Mechanical Engineering, 2021 , 34(4) : 58 -58 . DOI: 10.1186/s10033-021-00580-5

Abstract

For a single-structure deep learning fault diagnosis model, its disadvantages are an insufficient feature extraction and weak fault classification capability. This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy. First, a normal autoencoder, denoising autoencoder, sparse autoencoder, and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure. A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features. Finally, the advantage of the deep belief network probability model is used as the fault classifier to identify the faults. The effectiveness of the proposed method was verified by a gearbox test-bed. Experimental results show that, compared with traditional and existing intelligent fault diagnosis methods, the proposed method can obtain representative information and features from the raw data with higher classification accuracy.

Key words： Fault diagnosis; Feature fusion; Information entropy; Deep autoencoder; Deep belief network

参考文献

[1] B A Jaouher, F Nader, S Lotfi, et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 2015, 89: 16-27.
[2] J M Li, X F Yao, X D Wang, et al. Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis. Measurement, 2020, 153: 107419.
[3] K Adem, S Kili?arslan, O Comert. Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification. Expert Systems with Applications, 2019, 115: 557-564.
[4] F Mei, N Liu, H Y Miao, et al. On-line fault diagnosis model for locomotive traction inverter based on wavelet transform and support vector machine. Microelectronics Reliability, 2018, 88-90: 1274-1280.
[5] Y G Lei, Z J He, Y Y Zi. EEMD method and WNN for fault diagnosis of locomotive roller bearings. Expert Systems with Applications, 2011, 38(6): 7334-7341.
[6] Q Hu, X S Si, Q H Zhang, et al. A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests. Mechanical Systems and Signal Processing, 2020, 139: 106609.
[7] Z W Shang, X Liu, W X Li, et al. A rolling bearing fault diagnosis method based on fastDTW and an AGBDBN. Insight, 2020, 62: 457-463.
[8] F Shen, C Chen, J W Xu, et al. A fast multi-tasking solution: NMF-theoretic co-clustering for gear fault diagnosis under variable working conditions. Chinese Journal of Mechanical Engineering, 2020, 33: 16. https://doi.org/10.1186/s10033-020-00437-3.
[9] Y G Lei, F Jia, J Lin, et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3137-3147.
[10] H Wang, S Li, L Song, et al. An enhanced intelligent diagnosis method based on multi-sensor image fusion via improved deep learning network. IEEE Transactions on Instrumentation and Measurement, 2020, 69(6): 2648-2657.
[11] 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, 2016, 72-73: 303-315.
[12] Z H Zhang, S H Li, Y W Xiao, et al. Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning. Applied Energy, 2019, 233: 930-942.
[13] J B Yu. Evolutionary manifold regularized stacked denoising autoencoders for gearbox fault diagnosis. Knowledge-Based Systems, 2019, 178: 111-122.
[14] H D Shao, H K Jiang, X Q Li, et al. Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowledge-Based Systems, 2018, 140: 1-14.
[15] Z W Shang, X X Liao, R Geng, et al. Fault diagnosis method of rolling bearing based on deep belief network. Journal of Mechanical Science and Technology, 2018, 32(11): 5139-5145.
[16] X J Guo, L Chen, C Q Shen. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement, 2016, 93: 490-502.
[17] Z B Yang, J P Zhang, Z B Zhao, et al. Interpreting network knowledge with attention mechanism for bearing fault diagnosis. Applied Soft Computing, 2020, 97: 106829.
[18] W Zhang, G H Li, G L Peng, et al. 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.
[19] H D Shao, H K Jiang, Y Lin, et al. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mechanical Systems and Signal Processing, 2018, 102: 278-297.
[20] C C Che, H W Wang, X M Ni, et al. Hybrid multimodal fusion with deep learning for rolling bearing fault diagnosis. Measurement, 2020, 108655.
[21] J L Xiao. SVM and KNN ensemble learning for traffic incident detection. Physica A: Statistical Mechanics and its Applications, 2018, 517: 29-35.
[22] Y P Wu, W D Jin, J X Ren, et al. A multi-perspective architecture for high-speed train fault diagnosis based on variational mode decomposition and enhanced multi-scale structure. Applied Intelligence, 2019, 49(11): 3923-3937.
[23] J D Zheng, H Y Pan, J S Cheng. Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mechanical Systems and Signal Processing, 2017, 85: 746-759.
[24] B Duan, Z Y Li, P W Gu, et al. Evaluation of battery inconsistency based on information entropy. Journal of Energy Storage, 2018, 16: 160-166.
[25] Y Bengio, A Courville, P Vincent. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(8): 1798-1828.
[26] D E Rumelhart, G E Hinton, R J Williams. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533-536.
[27] P Vincent, H Larochelle, I Lajoie, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 2010, 11(12): 3371-3408.
[28] P Tamilselvan, P F Wang. Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety, 2013, 115: 124-135.
[29] G F Liu, H Q Bao, B K Han. A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis. Mathematical Problems in Engineering, 2018, 2018: 5105709.
[30] Z L Chen, Z N Li. Fault diagnosis method of rotating machinery based on stacked denoising autoencoder. Journal of Intelligent & Fuzzy Systems, 2018, 34(6): 3443-3449.
[31] M Sohaib, J M Kim. Reliable fault diagnosis of rotary machine bearings using a stacked sparse autoencoder-based deep neural network. Shock and Vibration, 2018, 2018: 2919637.
[32] C Q Shen, Y M Qi, J Wang, et al. An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder. Engineering Applications of Artificial Intelligence, 2018, 76: 170-184.
[33] D K Appana, A Prosvirin, J M Kim. Reliable fault diagnosis of bearings with varying rotational using envelope spectrum and convolution neural networks. Soft Computing, 2018, 22(20): 6719-6729.
[34] J X Qu, Z S Zhang, T Gong. A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion. Neurocomputing, 2016, 171: 837-853.
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