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Cluster Computing
Published in: Cluster Computing 3/2019

02-02-2018

Cost-sensitive large margin distribution machine for fault detection of wind turbines

Authors: Mingzhu Tang, Steven X. Ding, Chunhua Yang, Fanyong Cheng, Yuri A. W. Shardt, Wen Long, Daifei Liu

Published in: Cluster Computing | Special Issue 3/2019

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Abstract

Given the importance of the class-imbalanced data and misclassified unequal costs in large wind turbine datasets, this paper proposes a cost-sensitive large margin distribution machine (CLDM) for fault detection of wind turbines. The margin mean and margin variance are use to characterize the margin distribution. The objective function and constraints of the large margin distribution machine (LDM) are modified to be cost-sensitive. The class-imbalanced data and misclassified unequal costs are solved by selecting the appropriately cost-sensitive parameters. Then CLDM is designed to train and test data from wind turbines in a wind farm. In order to verify the effectiveness of CLDM, it is compared with support vector machine (SVM), cost-sensitive SVM, and LDM. Comprehensive experiments on 7 datasets from a benchmark model of wind turbines and 5 datasets from a real wind farm show that CLDM has better sensitivity, gMean and average misclassified cost than the other methods.
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Literature
1.
Zhang, X., Chen, W., Wang, B., Chen, X.: Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization. Neurocomputing 167, 260–279 (2015)CrossRef
2.
Houari, T., Moamar, S.M.: Hybrid dynamic classifier for drift-like fault diagnosis in a class of hybrid dynamic systems: application to wind turbine converters. Neurocomputing 171, 1496–1516 (2016)CrossRef
3.
Yin, Z., Hou, J.: Recent advances on svm based fault diagnosis and process monitoring in complicated industrial processes. Neurocomputing 174, 643–650 (2016)CrossRef
4.
Kusiak, A., Li, W.: The prediction and diagnosis of wind turbine faults. Renew. Energy 36(1), 16–23 (2011)CrossRef
5.
Dao, P.B., Staszewski, W.J., Barszcz, T., Uhl, T.: Condition monitoring and fault detection in wind turbines based on cointegration analysis of scada data. Renew. Energy 116, 107–122 (2017)CrossRef
6.
Badihi, H., Zhang, Y., Hong, H.: Fault-tolerant cooperative control in an offshore wind farm using model-free and model-based fault detection and diagnosis approaches. Appl. Energy 201, 284–307 (2017)CrossRef
7.
de Bessa, I.V., Palhares, R.M., D’Angelo, M.F.S.V., Chaves Filho, J.E.: Data-driven fault detection and isolation scheme for a wind turbine benchmark. Renew. Energy 87(1), 634–645 (2016)CrossRef
8.
Sebastian, T.S., Carlos, O.M., Puig, V.: Robust fault diagnosis of nonlinear systems using interval constraint satisfaction and analytical redundancy relations. IEEE Trans. Syst. Man Cybern. Syst. 44(1), 18–29 (2014)CrossRef
9.
Krüger, M., Ding, S.X., Haghani, A., Engel, P., Jeinsch, T.: A datadriven approach for sensor fault diagnosis in gearbox of wind energy conversion system. In: 2013 10th IEEE International Conference on Control and Automation (ICCA). IEEE, pp. 227–232 (2013)
10.
Chen, W., Ding, S.X., Haghani, A., Naik, A., Khan, A.Q., Yin, S.: Observer-based fdi schemes for wind turbine benchmark. IFAC Proc. Vol. 44(1), 7073–7078 (2011)CrossRef
11.
Badihi, H., Zhang, Y., Hong, H.: Wind turbine fault diagnosis and fault-tolerant torque load control against actuator faults. IEEE Trans. Control Syst. Technol. 23(4), 1351–1372 (2015)CrossRef
12.
Tabatabaeipour, S.M., Odgaard, P.E., Bak, T., Stoustrup, J.: Fault detection of wind turbines with uncertain parameters: a set-membership approach. Energies 5(7), 2224–2248 (2012)CrossRef
13.
Badihi, H., Zhang, Y., Hong, H.: Fuzzy gain-scheduled active fault tolerant control of a wind turbine. J. Frankl. Inst. 351(7), 3677–3706 (2014)CrossRef
14.
Sun, P., Li, J., Wang, C., Lei, X.: A generalized model for wind turbine anomaly identification based on scada data. Appl. Energy 168, 550–567 (2016)CrossRef
15.
Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. ACM Sigkdd Explor. Newsl. 6(1), 1–6 (2004)CrossRef
16.
Sun, Y., Kamel, M.S., Wong, A.K., Wang, Y.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recognit. 40(12), 3358–3378 (2007)CrossRef
17.
Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: MLSMOTE: approaching imbalanced multilabel learning through synthetic instance generation. Knowl. Based Syst. 89, 385–397 (2015)CrossRef
18.
Dez Pastor, J.F., Rodrguez, J.J., Garca Osorio, C., Kuncheva, L.I.: Random balance: ensembles of variable priors classifiers for imbalanced data. Knowl. Based Syst. 85, 96–111 (2015)CrossRef
19.
20.
Ling, C.X., Sheng, V.S.: Cost-Sensitive Learning, pp. 231–235. Springer, New York (2011)
21.
Davenport, M.A.: The 2nu-SVM: a cost-sensitive extension of the nu-SVM. DTIC Document, Report (2005)
22.
Gu, X., Chung, F.L., Ishibuchi, H.: Imbalanced TSK fuzzy classifier by cross-class Bayesian fuzzy clustering and imbalance learning. IEEE Trans. Syst. Man Cybernet. Syst. 47, 2005–2020 (2017)CrossRef
23.
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging, boosting, and hybrid-based approaches. IEEE Trans. Syst. Man Cybernet. Part C 42(4), 463–484 (2012)CrossRef
24.
Khoshgoftaar, T.M., Hulse, J.V., Napolitano, A.: Comparing boosting and bagging techniques with noisy and imbalanced data. IEEE Trans. Syst. Man Cybernet. Part A 41(3), 552–568 (2011)CrossRef
25.
Seiffert, C., Khoshgoftaar, T.M., Hulse, J.V., Napolitano, A.: Rusboost: a hybrid approach to alleviating class imbalance. IEEE Trans. Syst. Man Cybernet. Part A 40(1), 185–197 (2010)CrossRef
26.
Gu, B., Sun, X., Sheng, V.S.: Structural minimax probability machine. IEEE Trans. Neural Netw. Learn. Syst. 28, 1646–1656 (2017)MathSciNetCrossRef
27.
Yuan, C., Sun, X., Lv, R.: Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun. 13(7), 60–65 (2016)CrossRef
28.
Xia, Z., Wang, X., Sun, X., Wang, B.: Steganalysis of least significant bit matching using multi-order differences. Secur. Commun. Netw. 7(8), 1283–1291 (2014)CrossRef
29.
Xia, Z., Wang, X., Sun, X., Liu, Q., Xiong, N.: Steganalysis of LSB matching using differences between nonadjacent pixels. Multimed. Tools Appl. 75(4), 1947–1962 (2016)CrossRef
30.
Cheng, F., Zhang, J., Wen, C.: Cost-sensitive large margin distribution machine for classification of imbalanced data. Pattern Recognit. Lett. 80, 107–112 (2016)CrossRef
31.
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)CrossRef
32.
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, Cambridge (2001)
33.
Sun, J., Li, H., Adeli, H.: Concept drift-oriented adaptive and dynamic support vector machine ensemble with time window in corporate financial risk prediction. IEEE Trans. Syst. Man Cybernet. Syst. 43(4), 801–813 (2013)CrossRef
34.
Gu, B., Sheng, V.S.: A robust regularization path algorithm for support vector classification. IEEE Trans. Neural Netw. Learn. Syst. 28, 1241–1248 (2017)CrossRef
35.
Gu, B., Sheng, V.S., Tay, K.Y., Romano, W., Li, S.: Incremental support vector learning for ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. 26(7), 1403 (2015)MathSciNetCrossRef
36.
Gu, B., Sheng, V.S., Wang, Z., Ho, D., Osman, S., Li, S.: Incremental learning for support vector regression. Neural Netw. 67, 140–150 (2015)CrossRef
37.
Wymore, M.L., Van Dam, J.E., Ceylan, H., Qiao, D.: A survey of health monitoring systems for wind turbines. Renew. Sustain. Energy Rev. 52, 976–990 (2015)CrossRef
38.
Liu, W., Wang, Z., Han, J., Wang, G.: Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM. Renew. Energy 50, 1–6 (2013)CrossRef
39.
Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
40.
Zhang, T., Zhou, Z.: Large margin distribution machine. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, pp. 313–322 (2014)
41.
42.
Yuan, G., Ho, C., Lin, C.: Recent advances of large-scale linear classification. Proc. IEEE 100(9), 2584–2603 (2012)CrossRef
43.
Chang, K., Hsieh, C., Lin, C., Keerthi, S., Sundararajan, S.: A dual coordinate descent method for large-scale linear SVM. In: Proceedings of the 25th International Conference on Machine Learning, Conference Proceedings. ACM, New York (2008)
44.
Odgaard, P.F., Stoustrup, J., Kinnaert, M.: Fault-tolerant control of wind turbines: a benchmark model. IEEE Trans. Control Syst. Technol. 21(4), 1168–1182 (2013)CrossRef
Metadata
Title
Cost-sensitive large margin distribution machine for fault detection of wind turbines
Authors
Mingzhu Tang
Steven X. Ding
Chunhua Yang
Fanyong Cheng
Yuri A. W. Shardt
Wen Long
Daifei Liu
Publication date
02-02-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 3/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-1854-3

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