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2021 | OriginalPaper | Buchkapitel

Comparison of CWRU Dataset-Based Diagnosis Approaches: Review of Best Approaches and Results

verfasst von : Xiao Wei, Dirk Söffker

Erschienen in: European Workshop on Structural Health Monitoring

Verlag: Springer International Publishing

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Abstract

Bearings are the most common mechanical components in machines. Once a bearing fails (or components in it), other adjacent components or the machine itself are effected up to failure. Therefore, bearing health condition is of great interest in practice. Several benchmark datasets are developed to evaluate development in bearings health state (diagnosis) and remaining useful lifetime (prognosis). Among these datasets, Case Western Reserve University (CWRU) dataset is one of the most cited ones used to validate the performance of different diagnostic approaches. Over recent years, a significant amount of research approaches are developed using CWRU data. Most approaches are focused on specific performance parameters like detection rate or accuracy etc. The main problems in connection with CWRU dataset use are: no overview about latest results is available. Furthermore several results published are not complete, for example published accuracies rate without false alarm rates.

In this contribution an overview about the development change over the last years, the approaches applied, and specifically the results obtained will be given. Additionally, the new approaches emerging in recent years like deep learning (DL) also in combination with fusion methods and related performance will be given in comparison with conventional machine learning (ML) methods. Special care will be given to the completeness of published results also in combination with shown robustness. As outcome of this contribution the newest and best results are noted, furthermore a recommendation how to complete research work using benchmark dataset will be given. Although most approaches using CWRU dataset as benchmark get high accuracy, for further bearing fault diagnosis research, more and more suitable measures as well as other datasets are needed for increased performance evaluation.

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Vorheriges Kapitel Recent Results in Active and Passive SHM

Nächstes Kapitel Analyzing the Robustness of Hybrid, Output-Only, Kalman Filtering–Based System Identification Method

Shao, H., Jiang, H., Lin, Y., Li, X.: A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mech. Syst. Signal Process. 102, 278–297 (2018)CrossRef

Zhang, S., Zhang, S., Wang, B., Habetler, T.G.: Deep learning algorithms for bearing fault diagnostics—a comprehensive review. IEEE Access 8, 29857–29881 (2020)CrossRef

Boudiaf, A., Moussaoui, A., Dahane, A., Atoui, I.: A comparative study of various methods of bearing faults diagnosis using the case western reserve university data. J. Fail. Anal. Prev. 16(2), 271–284 (2016)CrossRef

Song, W., Lai, M., Li, X., Song, Y., Gao, L.: A new spectral clustering based on particle swarm optimization for unsupervised fault diagnosis of bearings. In: 15th International Conference on Automation Science and Engineering, pp. 386–391. IEEE, Vancouver (2019)

Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. Lect. Notes Comput. Sci. 1, 1015–1021 (2006)CrossRef

Zhang, B., Li, W., Li, X.L., Ng, S.K.: Intelligent fault diagnosis under varying working conditions based on domain adaptive convolutional neural networks. IEEE Access 6, 66367–66384 (2018)CrossRef

https://csegroups.case.edu/bearingdatacenter/pages/apparatus-procedures

Wen, L., Li, X., Gao, L., Zhang, Y.: A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans. Ind. Electron. 65(7), 5990–5998 (2018)CrossRef

Zhang, X., Liang, Y., Zhou, J., Zang, Y.: A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement 69, 164–179 (2015)CrossRef

10.

Li, X., Yang, Y., Pan, H., Cheng, J., Cheng, J.: A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis. Comput. Ind. 110, 36–47 (2019)CrossRef

11.

Jiang, Q., Chang, F.: A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine. J. Mech. Sci. Technol. 33(4), 1535–1543 (2019)CrossRef

12.

Nguyen, V.H., Cheng, J.S., Thai, V.T.: An intergrated generalized discriminant analysis method and chemical reaction support vector machine model. Adv. Prod. Eng. Manag. 12(4), 321–336 (2017)

13.

Saidi, L., Ali, J.B., Fnaieh, F.: Application of higher order spectral and support vector machines for bearing faults classification. ISA Trans. 54, 193–206 (2015)CrossRef

14.

Yadav, O.P., Pahuja, G.L.: Bearing fault detection using logarithmic wavelet packet transform and support vector machine. Image Graph. Sig. Process. 5, 21–33 (2019)

15.

Thelaidjia, T., Moussaoui, A., Chenikher, S.: Bearing fault diagnosis based on independent component analysis and optimized support vector machine. In: 7th International Conference on Modeling, Identification and Control, pp. 18–20. IEEE, Tunisia (2015)

16.

Ziani, R., Felkaoui, A., Zegadi, R.: Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion. J. Intell. Manuf. 28(2), 405–417 (2014)CrossRef

17.

Wu, C., Chen, T., Jiang, R.: Bearing fault diagnosis via kernel matrix construction based support vector machine. Vibroengineering 19(5), 3445–3461 (2017)CrossRef

18.

Pan, L., Xiao, J., Hu, S.: Intelligent fault diagnosis of rotating machinery using support vector machine and improved ABC. Adv. Comput. Sci. Res. 19, 388–393 (2019)

19.

Zheng, J., Pan, H., Cheng, J.: Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mech. Syst. Sig. Process. 85, 746–759 (2017)CrossRef

20.

Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetMATHCrossRef

21.

Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRef

22.

Wang, X., Li, Y., Rui, T., Zhu, H., Fei, J.: Bearing fault diagnosis method based on Hilbert envelope spectrum and deep belief network. Vibroengineering 17(3), 1295–1308 (2015)

23.

Liang, T., Shuaipeng, W., Duan, W., Zhang, R.: Bearing fault diagnosis based on improved ensemble learning and deep belief network. J. Phys.: Conf. Ser. 1074, 1–7 (2018)

24.

Yang, T., Huang, S.: Fault diagnosis based on improved deep belief network. In: 5th International Conference on Enterprise Systems, pp. 305–310. IEEE, Beijing (2017)

25.

Pan, T., Chen, J., Zhou, Z.: Intelligent fault diagnosis of rolling bearing via deep-layerwise feature extraction using deep belief network. In: International Conference on Sensing, Diagnostics, Prognostics and Control, pp. 509–514. IEEE, Xi’an (2018)

26.

Shao, H., Jiang, H., Wang, F., Wang, Y.: Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet. ISA Trans. 69, 187–201 (2017)CrossRef

27.

Qiao, H., Wang, T., Wang, P., Zhang, L., Mingda, X.: An adaptive weighted multiscale convolutional neural network for rotating machinery fault diagnosis under variable operating conditions. IEEE Access 7, 118954–118964 (2019)CrossRef

28.

Li, S., Liu, G., Tang, X., Jianguang, L., Jianjun, H.: An ensemble deep convolutional neural network model with improved D-S evidence fusion for bearing fault diagnosis. Sensor 17, 1729 (2017)CrossRef

29.

You, W., Shen, C., Guo, X., Zhu, Z.: Bearing fault diagnosis using convolutional neural network and support vector regression. In: International Conference on Mechanical Engineering and Control Automation, pp. 6–11 (2017)

30.

Xia, M., Li, T., Xu, L., Liu, L., de Silva, C.W.: Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Trans. Mechatron. 23(1), 101–110 (2018)CrossRef

31.

Yi, J., Fu, S., Cui, S., Zhao, C.: A deep contractive auto-encoding network for machinery fault diagnosis. In: The 18th International Symposium on Communications and Information Technologies, pp. 85–89. IEEE, Bangkok (2018)

32.

Wen, L., Gao, L., Li, X.: A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Trans. Syst. Man Cybern. 49(1), 136–144 (2019)CrossRef

33.

Shao, H., Jiang, H., Lin, Y., Li, X.: A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mech. Syst. Sig. Process. 102, 278–297 (2018)CrossRef

34.

Zhang, J., Chen, L., Li, Q., Zhang, Y.: An unsupervised method for rolling bearing fault diagnosis based on clustering and stacked auto-encoder. In: 2018 International Conference on Sensing, Diagnostics, Prognostics and Control, pp. 1–5. IEEE, Xi’an (2018)

35.

Di, J., Wang, L.: Application of improved deep auto-encoder network in rolling bearing fault diagnosis. J. Comput. Commun. 6, 41–53 (2018)CrossRef

36.

Xu, F., Tse, P.W.: Automatic roller bearings fault diagnosis using DSAE in deep learning and CFS algorithm. Soft. Comput. 23(13), 5117–5128 (2018)CrossRef

37.

Dai, J., Tang, J., Shao, F., Huang, S., Wang, Y.: Fault diagnosis of rolling bearing based on multiscale intrinsic mode function permutation entropy and a stacked sparse denoising autoencoder. Appl. Sci. 9, 27–43 (2019)

38.

Yu, H., Wang, K., Li, Y., Zhao, W.: Representation learning with class level autoencoder for intelligent fault diagnosis. IEEE Sig. Process. Lett. 26(10), 1476–1480 (2019)CrossRef

39.

Liu, W., Guo, P., Ye, L.: A low-delay lightweight recurrent neural network (LLRNN) for rotating machinery fault diagnosis. Sensors 19, 3109 (2019)CrossRef

40.

Zhang, A., Li, S., Cui, Y., Yang, W., Dong, R., Jianjun, H.: Limited data rolling bearing fault diagnosis with few-shot learning. IEEE Access 7, 110895–110904 (2019)CrossRef

Titel: Comparison of CWRU Dataset-Based Diagnosis Approaches: Review of Best Approaches and Results
verfasst von: Xiao Wei
Dirk Söffker
Verlag: Springer International Publishing
Buch: European Workshop on Structural Health Monitoring
Print ISBN: 978-3-030-64593-9

Electronic ISBN: 978-3-030-64594-6

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-64594-6_51

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