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ISSN (Online): 2210-2914

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Research Article Section: Remote Sensing

High-Performance Fault Classification Based on Feature Importance Ranking-XgBoost Approach with Feature Selection of Redundant Sensor Data

Author(s): Jilun Tian, Yuchen Jiang, Jiusi Zhang, Zhenhua Wang, Juan J. Rodríguez-Andina and Hao Luo*

Volume 2, Issue 3, 2022

Published on: 26 April, 2022

Page: [243 - 251] Pages: 9

DOI: 10.2174/2210298102666220318100051

Price: $65

Abstract

Background: Through the analysis of the relevant data of industrial equipment, faults diagnosis is helpful for system maintenance and reducing economic losses.

Objective: This study aimed at reducing the influence of irrelevant features and efficiently training the FIR-XgBoost model.

Methods: An Extreme Gradient Boosting (XgBoost) approach based on feature importance ranking (FIR) is proposed in this article for fault classification of high-dimensional complex industrial systems. Gini index is applied to rank the features according to the importance, and feature selection is implemented based on their position in the ranking.

Results: The dataset from the PHM 2021 data challenge, which is related to the process of fuse thermal imaging, is used. The classification accuracy of FIR-XgBoost has been found to be 99.63%, outperforming other existing algorithms. A case study is presented to show that excellent fault classification can be achieved through ensemble learning and feature selection.

Conclusion: Data-driven machine learning methods are proposed for solving high-dimensional fault classification problems on the dataset of the PHM2021 Data Challenge. An FIR-XgBoost method is proposed, the core of which is to retain important features and to reduce redundancy of sensor data. Consequently, feature selection based on FIR has better interpretability than other algorithms. Furthermore, the FIR- XgBoost algorithm retaining the 50 most important features has been observed to achieve the best fault classification performance among the compared algorithms and can be implemented in specific industrial processes.

Keywords: Fault classification, ensemble learning, feature importance ranking, PHM 2021 data challenge, XgBoost, algorithm.

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[1]
Peng, Y.; Liu, D.; Peng, X. A review: Prognostics and health management. J. Electr. Measur. Instr., 2010, 24(1), 1-9.
[http://dx.doi.org/10.3724/SP.J.1187.2010.00001]
[2]
Luo, H.; Yin, S.; Liu, T.; Khan, A.Q. A data-driven realization of the control-performance-oriented process monitoring system. IEEE Trans. Ind. Electron., 2020, 67(1), 521-530.
[http://dx.doi.org/10.1109/TIE.2019.2892705]
[3]
Jiang, Y.; Yin, S.; Li, K.; Luo, H.; Kaynak, O. Industrial applications of digital twins. Philos. Trans.- Royal Soc., Math. Phys. Eng. Sci., 2021, 379(2207), 20200360.
[http://dx.doi.org/10.1098/rsta.2020.0360] [PMID: 34398651]
[4]
Jiang, Y.; Yin, S.; Dong, J.; Kaynak, O.O. Kaynak. A review on soft sensors for monitoring, control and optimization of industrial pro-cesses. IEEE Sens. J., 2021, 21(11), 12868-12881.
[http://dx.doi.org/10.1109/JSEN.2020.3033153]
[5]
Jiang, Y.; Yin, S.; Kaynak, O. Performance supervised plant-wide process monitoring in industry 4.0: A roadmap. IEEE Open J. Ind. Electron. Soc., 2021, 2, 21-35.
[http://dx.doi.org/10.1109/OJIES.2020.3046044]
[6]
Feng, L.; Zhao, C. Fault description based attribute transfer for zero-sample industrial fault diagnosis. IEEE Trans. Industr. Inform., 2020, 99, 1-1.
[http://dx.doi.org/10.1109/TII.2020.2998086]
[7]
Deng, F.; Guo, S.; Zhou, R.; Chen, J. Sensor multifault diagnosis with improved support vector machines. IEEE Trans. Autom. Sci. Eng., 2017, 14(2), 1053-1063.
[http://dx.doi.org/10.1109/TASE.2015.2487523]
[8]
Nie, X.; Xie, G. A fault diagnosis framework insensitive to noisy labels based on recurrent neural network. IEEE Sens. J., 2021, 21(3), 2676-2686.
[http://dx.doi.org/10.1109/JSEN.2020.3023748]
[9]
Zhang, J.; Jiang, Y.; Luo, H.; Yin, S. Prediction of material removal rate in chemical mechanical polishing via residual convolutional neural network. Control Eng. Pract., 2021, 107, 104673.
[http://dx.doi.org/10.1016/j.conengprac.2020.104673]
[10]
Xiong, J.; Zhang, Q.; Sun, G.; Zhu, X.; Liu, M.; Li, Z. An information fusion fault diagnosis method based on dimensionless indicators with static discounting factor and knn. IEEE Sens. J., 2016, 16(7), 2060-2069.
[http://dx.doi.org/10.1109/JSEN.2015.2497545]
[11]
Cai, B.; Huang, L.; Xie, M. Bayesian networks in fault diagnosis. IEEE Trans. Industr. Inform., 2017, 13(5), 2227-2240.
[http://dx.doi.org/10.1109/TII.2017.2695583]
[12]
Zhang, F.Ye.Z.; Chakrabarty, K.; Gu, X. Adaptive board-level functional fault diagnosis using incremental decision trees. IEEE Trans. Comput. Aided Des. Integrated Circ. Syst., 2016, 35(2), 323-336.
[http://dx.doi.org/10.1109/TCAD.2015.2459046]
[13]
Xie, J. Li., Z.; Zhou, Z.; Liu, S. A novel bearing fault classification method based on xgboost: The fusion of deep learning-based features and empirical features. IEEE Trans. Instrum. Meas., 2020, 70, 1-9.
[14]
Roy, S.S.; Dey, S.; Chatterjee, S. Autocorrelation aided random forest classifier based bearing fault detection framework. IEEE Sens. J., 2020, 20(18), 10792-10800.
[http://dx.doi.org/10.1109/JSEN.2020.2995109]
[15]
Lei, Y.; Jia, F.; Lin, J.; Xing, S.; Ding, S.X. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans. Ind. Electron., 2016, 63(5), 3137-3147.
[http://dx.doi.org/10.1109/TIE.2016.2519325]
[16]
Chai, Z.; Zhao, C. Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification. IEEE Trans. Industr. Inform., 2020, 16(1), 54-66.
[http://dx.doi.org/10.1109/TII.2019.2915559]
[17]
Chen, G.; Liu, Y.; Ge, Z. K-means bayes algorithm for imbalanced fault classification and big data application. J. Process Contr., 2019, 81, 54-64.
[http://dx.doi.org/10.1016/j.jprocont.2019.06.011]
[18]
Zhang, D.; Qian, L.; Mao, B.; Huang, C.; Huang, B.; Si, Y. A data-driven design for fault detection of wind turbines using random forests and xgboost. IEEE Access, 2018, 6, 21020-21031.
[http://dx.doi.org/10.1109/ACCESS.2018.2818678]
[19]
Cheng, Y.; Qiao, X.; Wang, X.; Yu, Q. Random forest classifier for zero-shot learning based on relative attribute. IEEE Trans. Neural Netw. Learn. Syst., 2018, 29(5), 1662-1674.
[http://dx.doi.org/10.1109/TNNLS.2017.2677441] [PMID: 28333644]
[20]
Bentéjac, C.; Csrg, A. A comparative analysis of gradient boosting algorithms. Artif. Intell. Rev., 2021, 54(3), 1937-1967.
[http://dx.doi.org/10.1007/s10462-020-09896-5]
[21]
Wu, J.; Pan, S.; Zhu, X.; Cai, Z. Boosting for multi-graph classification. IEEE Trans. Cybern., 2015, 45(3), 430-443.
[PMID: 25014984]
[22]
Megantara, A.A.; Ahmad, T. Feature importance ranking for increasing performance of intrusion detection system. 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE), 2020, pp. 37-42.
[23]
Luca, B.; Mario, R.; Siavash, B.; Iason, K.; Danilo, G.; Daniel, G. "PHME Data Challenge" European conference of the prognostics and health management society 2021. Available from: https://phm-europe.org/data-challenge
[24]
Yang, C.; Veiga, C.; Rodriguez-Andina, J.J.; Farina, J.; Iniguez, A.; Yin, S. Using PPG signals and wearable devices for atrial fibrillation screening. IEEE Trans. Ind. Electron., 2019, 66(11), 8832-8842.
[http://dx.doi.org/10.1109/TIE.2018.2889614]
[25]
Singh, S.; Majumdar, A. Non-intrusive load monitoring via multilabel sparse representation-based classification. IEEE Trans. Smart Grid, 2020, 11(2), 1799-1801.
[http://dx.doi.org/10.1109/TSG.2019.2938090]
[26]
Zhang, J.; Jiang, Y.; Wu, S.; Li, X.; Luo, H.; Yin, S. Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism. Reliab. Eng. Syst. Saf., 2022, 221, 108297.
[http://dx.doi.org/10.1016/j.ress.2021.108297]
[27]
Prokhorenkova, L.; Gusev, G.; Vorobev, A.; Dorogush, A.V.; Gulin, A. CatBoost: Unbiased boosting with categorical features. In: 32nd Conference on Neural Information Processing Systems, Montréal, Canada, NeurIPS, 2018.

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