An attention enhanced dilated CNN approach for cross-axis industrial robotics fault diagnosis
Tóm tắt
An industrial robot is a complex mechatronics system, whose failure is hard to diagnose based on monitoring data. Previous studies have reported various methods with deep network models to improve the accuracy of fault diagnosis, which can get an accurate prediction model when the amount of data sample is sufficient. However, the failure data is hard to obtain, which leads to the few-shot issue and the bad generalization ability of the model. Therefore, this paper proposes an attention enhanced dilated convolutional neural network (D-CNN) approach for the cross-axis industrial robotics fault diagnosis method. Firstly, key feature extraction and sliding window are adopted to pre-process the monitoring data of industrial robots before D-CNN is introduced to extract data features. And self-attention is used to enhance feature attention capability. Finally, the pre-trained model is used for transfer learning, and a small number of the dataset from another axis of the multi-axis industrial robot are used for fine-tuning experiments. The experimental results show that the proposed method can reach satisfactory fault diagnosis accuracy in both the source domain and target domain.
Tài liệu tham khảo
I. Eski, S. Erkaya, S. Savas, S. Yildirim, Fault detection on robot manipulators using artificial neural networks. Robot. Comput.-Integr. Manuf. 27(1), 115–123 (2011)
L.D. Evjemo, T. Gjerstad, E.I. Grtli, G. Sziebig, Trends in smart manufacturing: role of humans and industrial robots in smart factories. Curr. Robot. Rep. 1(2), 35–41 (2020)
H. Li, X. Lian, C. Guo, P. Zhao, Investigation on early fault classification for rolling element bearing based on the optimal frequency band determination. J. Intell. Manuf. 26(1), 189–198 (2015)
C. Chen, Y. Liu, X. Sun, S. Wang, C.D. Cairano-Gilfedder, S. Titmus, A.A. Syntetos, Reliability analysis using deep learning, in ASME IDETC-CIE (ASME, Quebec, 2018)
A. Tavakoli, L.D. Maria, B. Valecillos, D. Bartalesi, S. Bittanti, A machine learning approach to fault detection in transformers by using vibration data. IFAC-PapersOnLine 53(2), 13656–13661 (2020)
C. Yan, M. Li, W. Liu, Transformer fault diagnosis based on BP-AdaBoost and PNN series connection. Math. Probl. Eng. 2019, Article ID 1019845 (2019)
A. Science, Aircraft engine health prognostics based on logistic regression with penalization regularization and state-space-based degradation framework. Aerosp. Sci. Technol. 68, 345–361 (2017)
N. Khera, S.A. Khan, Prognostics of aluminum electrolytic capacitors using artificial neural network approach. Microelectron. Reliab. 81, 328–336 (2018)
D. Zhu, X. Song, J. Yang, Y. Cong, L. Wang, A bearing fault diagnosis method based on L1 regularization transfer learning and LSTM deep learning, in 2021 IEEE International Conference on Information Communication and Software Engineering (ICICSE) (IEEE, 2021)
D. Yang, K. Sun, A CAE-based deep learning methodology for rotating machinery fault diagnosis, in 2021 7th International Conference on Control, Automation and Robotics (ICCAR) (IEEE, 2021)
C. Zhang, L. Xu, X. Li, H. Wang, A method of fault diagnosis for rotary equipment based on deep learning, in 2018 Prognostics and System Health Management Conference (PHM-Chongqing) (IEEE, 2018)
Z. Dongzhu, Z. Hua, D. Shiqiang, S. Yafei, Aero-engine bearing fault diagnosis based on deep neural networks, in 2020 11th International Conference on Mechanical and Aerospace Engineering (ICMAE) (IEEE, 2020)
Z. Xu, W. Mo, Y. Wang, S. Luo, T. Liu, Transformer fault diagnosis based on deep brief sparse autoencoder, in 2019 Chinese Control Conference (CCC) (IEEE, 2019)
J. Jinghai, C. Weidong, Research on EMU cable fault diagnosis based on deep belief network and signal domain conversion, in 2021 4th International Conference on Electron Device and Mechanical Engineering (ICEDME) (IEEE, 2021)
W. Lihao, D. Yanni, A fault diagnosis method of tread production line based on convolutional neural network, in 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS) (IEEE, 2018)
G. Niu, S. Tang, B. Zhang, Machine condition prediction based on long short term memory and particle filtering, in IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society (IEEE, 2018)
A.A. Jaber, R. Bicker, Industrial robot backlash fault diagnosis based on discrete wavelet transform and artificial neural network. Am. J. Mech. Eng. 4(1), 21–31 (2016)
D. Brambilla, L.M. Capisani, A. Ferrara, P. Pisu, Fault detection for robot manipulators via second-order sliding modes. IEEE Trans. Ind. Electron. 55(11), 3954–3963 (2008)
Y. Kim, J. Park, K. Na, H. Yuan, B.D. Youn, C.-S. Kang, Phase-based time domain averaging (PTDA) for fault detection of a gearbox in an industrial robot using vibration signals. Mech. Syst. Signal Process. 138, 106544 (2020)
O. Janssens, V. Slavkovikj, B. Vervisch, K. Stockman, M. Loccufier, S. Verstockt, R. Van de Walle, S. Van Hoecke, Convolutional neural network based fault detection for rotating machinery. J. Sound Vib. 377, 331–345 (2016)
Z. Chen, K. Gryllias, W. Li, Mechanical fault diagnosis using convolutional neural networks and extreme learning machine. Mech. Syst. Signal Process. 133, 106272 (2019)
B. Gong, X. Du, Research on analog circuit fault diagnosis based on CBAM-CNN, in 2021 IEEE International Conference on Electronic Technology, Communication and Information (ICETCI) (IEEE, 2021)
Y. Hao, H. Wang, Z. Liu, H. Han, Multi-scale CNN based on attention mechanism for rolling bearing fault diagnosis, in 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM) (IEEE, 2020)
S. Yang, X. Kong, Q. Wang, Z. Li, H. Cheng, K. Xu, Deep multiple auto-encoder with attention mechanism network: a dynamic domain adaptation method for rotary machine fault diagnosis under different working conditions. Knowl.-Based Syst. 249, 108639 (2022)
Z. Zhang, L. Chen, C. Zhang, H. Shi, H. Li, GMA-DRSNs: a novel fault diagnosis method with global multi-attention deep residual shrinkage networks. Measurement 196, 111203 (2022)
C. Qian, J. Zhu, Y. Shen, Q. Jiang, Q. Zhang, Deep transfer learning in mechanical intelligent fault diagnosis: application and challenge. Neural Process. Lett., 1–23 (2022). https://doi.org/10.1007/s11063-021-10719-z
S. Liu, H. Wang, J. Tang, X. Zhang, Research on fault diagnosis of gas turbine rotor based on adversarial discriminative domain adaption transfer learning. Measurement 196, 111174 (2022)
J. Tian, D. Han, M. Li, P. Shi, A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis. Knowl.-Based Syst. 243, 108466 (2022)
T. Han, C. Liu, W. Yang, D. Jiang, A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowl.-Based Syst. 165, 474–487 (2019)
S. Pang, X. Yang, A cross-domain stacked denoising autoencoders for rotating machinery fault diagnosis under different working conditions. IEEE Access 7, 77277–77292 (2019)
H. Zheng, R. Wang, Y. Yang, Y. Li, M. Xu, Intelligent fault identification based on multisource domain generalization towards actual diagnosis scenario. IEEE Trans. Ind. Electron. 67(2), 1293–1304 (2019)
M.J. Hasan, M. Sohaib, J.-M. Kim, 1D CNN-based transfer learning model for bearing fault diagnosis under variable working conditions, in International Conference on Computational Intelligence in Information System (Springer, Berlin, 2019)
Y. Xu, Z. Li, S. Wang, W. Li, T. Sarkodie-Gyan, S. Feng, A hybrid deep-learning model for fault diagnosis of rolling bearings. Measurement 169, 108502 (2021)