Dynamic deep learning algorithm based on incremental compensation for fault diagnosis model
As one of research and practice hotspots in the field of intelligent manufacturing, the machine learning approach is applied to diagnose and predict equipment fault for running state data. Despite deep learning approach overcomes the problem that the traditional machine learning approaches for fault...
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| Published in | International journal of computational intelligence systems Vol. 11; no. 1; pp. 846 - 860 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.01.2018
Springer Nature B.V Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1875-6891 1875-6883 1875-6883 |
| DOI | 10.2991/ijcis.11.1.64 |
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| Abstract | As one of research and practice hotspots in the field of intelligent manufacturing, the machine learning approach is applied to diagnose and predict equipment fault for running state data. Despite deep learning approach overcomes the problem that the traditional machine learning approaches for fault diagnosis is difficult to characterize the complex mapping between the massive fault data, the exponentially grown and newly generated data is learned repeatedly, and these approaches cannot incrementally correct the model to adapt the situation that the states and properties of equipment are changed over time, resulting in the increase of time costs and the decrease of diagnosis accuracy of model. In this paper, a dynamic deep learning algorithm based on incremental compensation is proposed. Firstly, the feature modes of the newly generated data are extracted by using deep learning algorithm; it is then compared with the fault modes extracted from the historical data. Next, a similarity computing model is presented to dynamically adjust the weights of incrementally merged modes. Finally, the SVM algorithm is employed to classify the weighted modes by supervised way, and the BP algorithm utilized to fine tune the model, in order to complete the dynamic and compensatory adjustment of deep learning with original modes and incremental modes. The experimental results of bearing running data demonstrate that the proposed approach could significantly improve the accuracy of diagnosis and save the time cost, contributing to meet the varied needs of the real-time equipment fault diagnosis. |
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| AbstractList | As one of research and practice hotspots in the field of intelligent manufacturing, the machine learning approach is applied to diagnose and predict equipment fault for running state data. Despite deep learning approach overcomes the problem that the traditional machine learning approaches for fault diagnosis is difficult to characterize the complex mapping between the massive fault data, the exponentially grown and newly generated data is learned repeatedly, and these approaches cannot incrementally correct the model to adapt the situation that the states and properties of equipment are changed over time, resulting in the increase of time costs and the decrease of diagnosis accuracy of model. In this paper, a dynamic deep learning algorithm based on incremental compensation is proposed. Firstly, the feature modes of the newly generated data are extracted by using deep learning algorithm; it is then compared with the fault modes extracted from the historical data. Next, a similarity computing model is presented to dynamically adjust the weights of incrementally merged modes. Finally, the SVM algorithm is employed to classify the weighted modes by supervised way, and the BP algorithm utilized to fine tune the model, in order to complete the dynamic and compensatory adjustment of deep learning with original modes and incremental modes. The experimental results of bearing running data demonstrate that the proposed approach could significantly improve the accuracy of diagnosis and save the time cost, contributing to meet the varied needs of the real-time equipment fault diagnosis. |
| Author | Dou, Runliang Liu, Jing An, Yacheng Ji, Haipeng |
| Author_xml | – sequence: 1 givenname: Jing surname: Liu fullname: Liu, Jing organization: School of computer science and engineering, Hebei University of Technology, Hebei Key Laboratory of Dig data Calculation, The University of Iowa Engineering School – sequence: 2 givenname: Yacheng surname: An fullname: An, Yacheng organization: School of computer science and engineering, Hebei University of Technology – sequence: 3 givenname: Runliang surname: Dou fullname: Dou, Runliang email: drl@tju.edu.cn organization: College of Management and Economics, Tianjin University – sequence: 4 givenname: Haipeng surname: Ji fullname: Ji, Haipeng organization: The University of Iowa Engineering School, Research Institute for Energy Equipment Materials, Hebei University of Technology |
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| Copyright | the Authors. Published by Atlantis Press 2018 2018. This work is licensed under http://creativecommons.org/licences/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | Deep learning Fault diagnosis Dynamic compensation Incremental learning Denoising Autoencoder |
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| References | M. Yuwono, Y. Qin, J. Zhou, Y. Guo, B.G. Celler and S.W. Su, Automatic bearing fault diagnosis using particle swarm clustering and hidden Markov model, Engineering Applications of Artificial Intelligence, 47(SI) (2016)88– 100. A. Krizhevsky, I. Sutskever and G.E. Hinton, ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 25(2) (2012)1097–1105. L.C. Jain, M. Seera, C.P. Lim and P. Balasubramaniam, A review of online learning in supervised neural networks, Neural Computing and Applications, 25(3–4) (2014)491–509. M. Amar, I. Gondal and C. Wilson, Vibration spectrum imaging: A novel bearing fault classification approach, IEEE Transactions on Industrial Electronics, 62(1) (2014)494–502. G E Hinton and R S Zemel. Autoencoders, minimum description length and Helmholtz free energy, in International Conference on Neural Information Processing Systems (CA, San Francisco, 1993), pp.3–10. Y. Bengio, A. Courville and P. Vincent, Representation learning: A review and new perspectives, IEEE Transactions on Software Engineering, 35(8) (2013)1798 –1828. C. Li, R.V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera and R.E. Vásquez, Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals, Mechanical Systems & Signal Processing, 76–77(2016) 283–293. S. Yin and Z.H. Huang, Performance monitoring for vehicle suspension system via fuzzy positivistic C-means clustering based on accelerometer measurements, IEEE- ASME Transactions on Mechatronics, 20(5) (2015)2613– 2620. Z.X. Xu, M. Yao, Z.H. Wu and W.H. Dai, Incremental regularized extreme learning machine and it’s enhancement, Neurocomputing, 174(SI) (2016)134–142. D. Fernández-Francos, D. Martínez-Rego, O. Fontenla-Romero and A. Alonso-Betanzos, Automatic bearing fault diagnosis based on one-class ν-SVM, Computers & Industrial Engineering, 64(1) (2013)357–365. W.T. Mao, L. He, Y.J. Yan and J.W. Wang, Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine, Mechanical Systems & Signal Processing, 83(2017)450–473. P. Tamilselvan and P.F. Wang, Failure diagnosis using deep belief learning based health state classification, Reliability Engineering & Systems Safety, 115(2013)124– 135. J. Zheng, F.R. Shen, H.J. Fan and J.X. Zhao, An online incremental learning support vector machine for large-scale data, Neural Computing and Applications, 22(5) (2013)1023–1035. F. Jia, Y.G. Lei, J. Lin, X. Zhou and N. Lu, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mechanical Systems & Signal Processing, 72–73(2016)303–315. 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 & Signal Processing, 72–73(2016)92–104. P. Vincent, H. Larochelle, Y. Bengio and P.A. Manzagol, Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th international conference on Machine learning table of contents (Helsinki, Finland, 2008), pp. 1096–1103. Z. Cui, H. Chang, S.G. Shan, B.P. Ma and X.L. Chen, Joint sparse representation for video-based face recognition, Neurocomputing, 135(SI) (2014)306–312. Y.S. Wang, Q.H. Ma, Q. Zhu, X.T. Liu and L.H. Zhao, An intelligent approach for engine fault diagnosis based on Hilbert-Huang transform and support vector machine, Applied Acoustics, 75(2014)1–9. J. Liu and E. Zio, An adaptive online learning approach for Support Vector Regression: Online-SVR-FID, Mechanical Systems & Signal Processing, 76–77(2016) 796–809. K.A. Loparo, Bearings data center (Ohio, Cleveland, 2014), http: //www.eecs.cwru.edu/laboratory/bearings/download.htm. J. Liu, Y.F. Dong, Y. Li, S.Y. Lei and S.Q. He, Composite fault diagnosis and intelligent maintenance based on data driven, Key Engineering Materials, 685 (2016)247–250. H. Fernando and B. Surgenor, An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine, Robotics and Computer-Integrated Manufacturing, 43(SI) (2017)79–88. S Kullback and R A Leibler, On information and sufficiency, The Annals of Mathematical Statistics, 22(1) (1951)79–86. G. Yin, Y.T. Zhang, Z.N. Li, G.Q. Ren and H.B. Fan, Online fault diagnosis method based on Incremental Support Vector Data Description and Extreme Learning Machine with incremental output structure, Neurocomputing, 128(2014)224–231. H.H. Chen, P. Tiňo and X. Yao, Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space, Computers & Chemical Engineering, 67(2014)33–42. |
| References_xml | – reference: W.T. Mao, L. He, Y.J. Yan and J.W. Wang, Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine, Mechanical Systems & Signal Processing, 83(2017)450–473. – reference: Y.S. Wang, Q.H. Ma, Q. Zhu, X.T. Liu and L.H. Zhao, An intelligent approach for engine fault diagnosis based on Hilbert-Huang transform and support vector machine, Applied Acoustics, 75(2014)1–9. – reference: F. Jia, Y.G. Lei, J. Lin, X. Zhou and N. Lu, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mechanical Systems & Signal Processing, 72–73(2016)303–315. – reference: G E Hinton and R S Zemel. Autoencoders, minimum description length and Helmholtz free energy, in International Conference on Neural Information Processing Systems (CA, San Francisco, 1993), pp.3–10. – reference: Y. Bengio, A. Courville and P. Vincent, Representation learning: A review and new perspectives, IEEE Transactions on Software Engineering, 35(8) (2013)1798 –1828. – reference: H.H. Chen, P. Tiňo and X. Yao, Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space, Computers & Chemical Engineering, 67(2014)33–42. – reference: P. Vincent, H. Larochelle, Y. Bengio and P.A. Manzagol, Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th international conference on Machine learning table of contents (Helsinki, Finland, 2008), pp. 1096–1103. – reference: Z. Cui, H. Chang, S.G. Shan, B.P. Ma and X.L. Chen, Joint sparse representation for video-based face recognition, Neurocomputing, 135(SI) (2014)306–312. – reference: C. Li, R.V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera and R.E. Vásquez, Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals, Mechanical Systems & Signal Processing, 76–77(2016) 283–293. – reference: K.A. Loparo, Bearings data center (Ohio, Cleveland, 2014), http: //www.eecs.cwru.edu/laboratory/bearings/download.htm. – reference: J. Liu and E. Zio, An adaptive online learning approach for Support Vector Regression: Online-SVR-FID, Mechanical Systems & Signal Processing, 76–77(2016) 796–809. – reference: M. Amar, I. Gondal and C. Wilson, Vibration spectrum imaging: A novel bearing fault classification approach, IEEE Transactions on Industrial Electronics, 62(1) (2014)494–502. – reference: 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 & Signal Processing, 72–73(2016)92–104. – reference: Z.X. Xu, M. Yao, Z.H. Wu and W.H. Dai, Incremental regularized extreme learning machine and it’s enhancement, Neurocomputing, 174(SI) (2016)134–142. – reference: S Kullback and R A Leibler, On information and sufficiency, The Annals of Mathematical Statistics, 22(1) (1951)79–86. – reference: M. Yuwono, Y. Qin, J. Zhou, Y. Guo, B.G. Celler and S.W. Su, Automatic bearing fault diagnosis using particle swarm clustering and hidden Markov model, Engineering Applications of Artificial Intelligence, 47(SI) (2016)88– 100. – reference: J. Liu, Y.F. Dong, Y. Li, S.Y. Lei and S.Q. He, Composite fault diagnosis and intelligent maintenance based on data driven, Key Engineering Materials, 685 (2016)247–250. – reference: G. Yin, Y.T. Zhang, Z.N. Li, G.Q. Ren and H.B. Fan, Online fault diagnosis method based on Incremental Support Vector Data Description and Extreme Learning Machine with incremental output structure, Neurocomputing, 128(2014)224–231. – reference: H. Fernando and B. Surgenor, An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine, Robotics and Computer-Integrated Manufacturing, 43(SI) (2017)79–88. – reference: A. Krizhevsky, I. Sutskever and G.E. Hinton, ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 25(2) (2012)1097–1105. – reference: L.C. Jain, M. Seera, C.P. Lim and P. Balasubramaniam, A review of online learning in supervised neural networks, Neural Computing and Applications, 25(3–4) (2014)491–509. – reference: J. Zheng, F.R. Shen, H.J. Fan and J.X. Zhao, An online incremental learning support vector machine for large-scale data, Neural Computing and Applications, 22(5) (2013)1023–1035. – reference: D. Fernández-Francos, D. Martínez-Rego, O. Fontenla-Romero and A. Alonso-Betanzos, Automatic bearing fault diagnosis based on one-class ν-SVM, Computers & Industrial Engineering, 64(1) (2013)357–365. – reference: S. Yin and Z.H. Huang, Performance monitoring for vehicle suspension system via fuzzy positivistic C-means clustering based on accelerometer measurements, IEEE- ASME Transactions on Mechatronics, 20(5) (2015)2613– 2620. – reference: P. Tamilselvan and P.F. Wang, Failure diagnosis using deep belief learning based health state classification, Reliability Engineering & Systems Safety, 115(2013)124– 135. |
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| SubjectTerms | Algorithms Compensation Deep learning Denoising Autoencoder Dynamic compensation Fault diagnosis Feature extraction Incremental learning Intelligent manufacturing systems Machine learning Model accuracy Research Article Support vector machines |
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| Title | Dynamic deep learning algorithm based on incremental compensation for fault diagnosis model |
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