Abstract
This paper presents a real-time and reliable bearing fault diagnosis scheme for induction motors with optimal fault feature distribution analysis based discriminant feature selection. The sequential forward selection (SFS) with the proposed feature evaluation function is used to select the discriminative feature vector. Then, the k-nearest neighbor (k-NN) is employed to diagnose unknown fault signals and validate the effectiveness of the proposed feature selection and fault diagnosis model. However, the process of feature vector evaluation for feature selection is computationally expensive. This paper presents a parallel implementation of feature selection with a feature evaluation algorithm on a multi-core architecture to accelerate the algorithm. The optimal organization of processing elements (PE) and the proper distribution of feature data into memory of each PE improve diagnosis performance and reduce computational time to meet real-time fault diagnosis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Widodo, A., Kim, E.Y., Son, J.-D., Yang, B.-S., Tan, A.C.C., Gu, D.-S., Choi, B.-K., Mathew, J.: Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. J. Expert Syst. Appl. 36(3), 7252–7261 (2009). Part 2
Zhao, M., Jin, X., Zhang, Z., Li, B.: Fault diagnosis of rolling element bearings via discriminative subspace learning: visualization and classification. 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)
Namsrai, E., Munkhdalai, T., Li, M., Shin, J., Namsrai, O., Ryu, K.H.: A feature selection-based ensemble method for arrhythmia classification. J. Inf. Process. Syst. 9(1), 31–40 (2013)
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)
Rauber, T.W., de Assis Boldt, F., Flavio, M.V.: Heterogeneous feature models and feature selection applied to bearing fault diagnosis. IEEE Trans. Ind. Electron. 62(1), 637–646 (2015)
Kanan, H.R., Faez, K.: GA-based optimal selection of PZMI features for face recognition. Appl. Math. Comput. 205(2), 706–715 (2008)
Kang, M., Kim, J., Kim, J.-M.: reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm. Inf. Sci. 294, 423–438 (2015)
Kang, M., Kim, J., Kim, J.-M.: An FPGA-based multicore system for real-time bearing fault diagnosis using ultrasampling rate AE signals. IEEE Trans. Ind. Electron. 62(4), 2319–2329 (2015)
Seo, J., Kang, M., Kim, C.-H., Kim, J.-M.: An optimal many-core model based supercomputing for accelerating video-equipped fire detection. J. Supercomput. 71(6), 2275–2308 (2015)
Rashedul Islam, M., Khan, S.A., Kim, J.-M.: Maximum class separability-based discriminant feature selection using a GA for reliable fault diagnosis of induction motors. In: Huang, D.-S., Han, K. (eds.) ICIC 2015. LNCS, vol. 9227, pp. 526–537. Springer, Heidelberg (2015)
Yigit, H.: A weighting approach for KNN classifier. In: Proceedings of International Conference on Electronics, Computer and Computation, pp. 228–231 (2013)
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (Nos. NRF-2015K2A1A2070866 and NRF-2013R1A2A2A05004566).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Rashedul Islam, M., Sharif Uddin, M., Khan, S., Kim, JM., Kim, CH. (2016). Multi-core Accelerated Discriminant Feature Selection for Real-Time Bearing Fault Diagnosis. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-42007-3_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42006-6
Online ISBN: 978-3-319-42007-3
eBook Packages: Computer ScienceComputer Science (R0)