Abstract
Reliable fault diagnosis in bearing elements of induction motors, with high classification performance, is of paramount importance for ensuring steady manufacturing. The performance of any fault diagnosis system largely depends on the selection of a feature vector that represents the most distinctive fault attributes. This paper proposes a maximum class separability (MCS) feature distribution analysis-based feature selection method using a genetic algorithm (GA). The MCS distribution analysis model analyzes and selects an optimal feature vector, which consists of the most distinguishing features from a high dimensional feature space, for reliable multi-fault diagnosis in bearings. The high dimensional feature space is an ensemble of hybrid statistical features calculated from time domain analysis, frequency domain analysis, and envelope spectrum analysis of the acoustic emission (AE) signal. The proposed maximum class separability-based objective function using the GA is used to select the optimal feature set. Finally, k-nearest neighbor (k-NN) algorithm is used to validate our proposed approach in terms of the classification performance. The experimental results validate the superior performance of our proposed model for different datasets under different motor rotational speeds as compared to conventional models that utilize (1) the original feature vector and (2) a state-of-the-art average distance-based feature selection method.
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References
Zhao, M., Jin, X., Zhang, Z., Li, B.: Fault diagnosis of rolling element bearings via discriminative subspace learning: visualization and classification. J. Expert Syst. Appl. 41(7), 3391–3401 (2014)
Uddin, J., Islam, R., Kim, J.: Texture feature extraction techniques for fault diagnosis of induction motors. J. Convergence 5(2), 15–20 (2014)
Prieto, M.D., Cirrincione, G.A., Espinosa, G., Ortega, J.A., Henao, H.: Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Trans. Ind. Electron. 30(8), 3398–3407 (2013)
Yu, J.: Local and nonlocal preserving projection for bearing defect classification and performance assessment. IEEE Trans. Ind. Electron. 59(5), 2363–2376 (2012)
Bediaga, I., Mendizabal, X., Arnaiz, A., Munoa, J.: Ball bearing damage detection using traditional signal processing algorithms. IEEE Instrum. Meas. Magz. 16(2), 20–25 (2013)
Lau, E.C.C., Ngan, H.W.: Detection of motor bearing outer raceway defect by wavelet packet transformed motor current signature analysis. IEEE Trans. Instrum. Meas. 59(10), 2683–2690 (2010)
Kankar, P.K., Sharma, S.C., Harsha, S.P.: Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform. Neurocomput. 110, 9–17 (2013)
Konar, P., Chattopadhyay, P.: Bearing fault detection of induction motor using wavelet and support vector machines (SVMs). Appl. Soft Comput. 11(6), 4203–4211 (2011)
Rafiee, J., Rafiee, M.A., Tse, P.W.: Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Syst. Appl. 37(6), 4568–4579 (2010)
Rauber, T.W., de Assis Boldt, F., Flavio, M.V.: Heterogeneous feature models and feature selection applied to bearing fault diagnosis. IEEE Trans. Industr. Electron. 62(1), 637–646 (2015)
Mahrooghy, M., Nicolas, H.Y.: On the use of the genetic algorithm filter-based feature selection technique for satellite precipitation estimation. IEEE Geosci. Remote Sens. Lett. 9(5), 963–967 (2012)
Kanan, H.R., Faez, K.: GA-based optimal selection of PZMI features for face recognition. Appl. Math. Comput. 205(2), 706–715 (2008)
Nguyen, N.T., Lee, H.H., Kwon, J.: Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor. J. Mech. Sci. Technol. 22(3), 490–496 (2008)
Beasley, D., Bull, D.R., Martin, R.R.: An Overview of genetic algorithms: Part 1, fundamentals. Univ. Comput. 15(2), 58–69 (1993)
Yu, X., Shao, J., Dong, H.: On evolutionary strategy based on hybrid crossover operators. In: International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT), vol. 5, pp. 2355–2358 (2011)
Mudaliar, D.N., Modi, N.K.: Unraveling travelling salesman problem by genetic algorithm using m-crossover operator. In: 2013 International Conference on Signal Processing Image Processing & Pattern Recognition (ICSIPR), pp. 127–130 (2013)
Qi-yi, Z., Shu-chun, C.: An improved crossover operator of genetic algorithm. In: International Symposium on Computational Intelligence and Design, ISCID 2009, vol. 2, pp. 82–86 (2009)
Ouerfelli, H., Dammak, A.: The genetic algorithm with two point crossover to solve the resource-constrained project scheduling problems. In: 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO 2013), pp. 1–4 (2013)
Zhang, N., Yang, J., Qian, J.: Component-based global k-NN classifier for small sample size problems. Pattern Recogn. Lett. 33(13), 1689–1694 (2012)
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2013R1A2A2A05004566)
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Rashedul Islam, M., Khan, S.A., Kim, JM. (2015). Maximum Class Separability-Based Discriminant Feature Selection Using a GA for Reliable Fault Diagnosis of Induction Motors. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_56
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