Abstract
In this paper, a new hybrid intelligent technique is presented based on the improvement in the feature selection method for multi-fault classification. The bearing conditions used in this study include healthy condition, defective inner ring, defective outer ring, and the faulty rolling element at different rotating motor speeds. To form the feature matrix, at first, the vibration signals are decomposed using empirical mode decomposition and wavelet packet decomposition. Then, the time and frequency domain features are extracted from the raw signals and the components are obtained from the signal decomposition. The high-dimensional feature matrix leads to increasing the computational complexity and reducing the efficiency in the classification accuracy of faults. Therefore, in the first stage of the feature selection process, the redundant and unnecessary features are eliminated by the FDAF-score feature selection method and the preselected feature set is formed. The FDAF-score technique is a combination of both F-score and Fisher discriminate analysis (FDA) algorithms. Since there may exist the features that are not susceptible to the presence of faults, the binary particle swarm optimization (BPSO) algorithm and the support vector machine (SVM) are used to select the optimal features from the preselected features. The BPSO algorithm is used to determine the optimal feature set and SVM classifier parameters so that the predictive error of the bearing conditions and the number of selected features are minimized. The results obtained in this paper demonstrate that the selected features are able to differentiate the different bearing conditions at various speeds. Comparing the results of this article with other fault detection methods indicates the ability of the proposed method.
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References
Ali JB, Fnaiech N, Saidi L, Chebel-Morello B, Fnaiech F (2015) Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust 89:16–27. https://doi.org/10.1016/j.apacoust.2014.08.016
Attoui I, Fergani N, Boutasseta N, Oudjani B, Deliou A (2017) A new time–frequency method for identification and classification of ball bearing faults. J Sound Vib 397:241–265. https://doi.org/10.1016/j.jsv.2017.02.041
Banka H, Dara S (2015) A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation. Pattern Recognit Lett 52:94–100. https://doi.org/10.1016/j.patrec.2014.10.007
Bearing Data Center (2016) Case Western Reserve University. http://csegroups.case.edu/bearingdatacenter/home
Bhuyan HK, Kamila NK (2015) Privacy preserving sub-feature selection in distributed data mining. Appl Soft Comput 36:552–569. https://doi.org/10.1016/j.asoc.2015.06.060
Bordoloi DJ, Tiwari R (2014a) Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time–frequency vibration data. Measurement 55:1–14. https://doi.org/10.1016/j.measurement.2014.04.024
Bordoloi DJ, Tiwari R (2014b) Optimum multi-fault classification of gears with integration of evolutionary and SVM algorithms. Mech Mach Theory 73:49–60. https://doi.org/10.1016/j.mechmachtheory.2013.10.006
Bordoloi DJ, Tiwari R (2015) Optimisation of SVM methodology for multiple fault taxonomy of rolling bearings from acceleration records. In: Proceedings of the 9th IFToMM international conference on rotor dynamics, pp 533–542. https://doi.org/10.1007/978-3-319-06590-8_43
Chen G, Chen J (2015) A novel wrapper method for feature selection and its applications. Neurocomputing 159:219–226. https://doi.org/10.1016/j.neucom.2015.01.070
Fatima S, Mohanty AR, Naikan VNA (2015) Multiple fault classification using support vector machine in a machinery fault simulator. Vib Eng Technol Mach. https://doi.org/10.1007/978-3-319-09918-7_90
Frei MG, Osorio I (2007) Intrinsic time-scale decomposition: time-frequency–energy analysis and real-time filtering of non-stationary signals. Proc R Soc Lond A Math Phys Sci 463(2078):321–342. https://doi.org/10.1098/rspa.2006.1761
Fu W, Tan J, Zhang X, Chen T, Wang K (2019) Blind parameter identification of MAR model and mutation hybrid GWO-SCA optimized SVM for fault diagnosis of rotating machinery. Complexity. https://doi.org/10.1155/2019/3264969
Gilles J (2013) Empirical wavelet transform. IEEE T Signal Process 61(16):3999–4010. https://doi.org/10.1109/TSP.2013.2265222
Guo T, Deng Z (2017) An improved EMD method based on the multi-objective optimization and its application to fault feature extraction of rolling bearing. Appl Acoust 127:46–62. https://doi.org/10.1016/j.apacoust.2017.05.018
Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3):389–422. https://doi.org/10.1023/A:1012487302797
Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425. https://doi.org/10.1109/72.991427
Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31(2):231–240. https://doi.org/10.1016/j.eswa.2005.09.024
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A Math Phys Sci 454(1971):903–995. https://doi.org/10.1098/rspa.1998.0193
Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541
Jedliński L, Jonak J (2015) Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform. Appl Soft Comput 30:636–641. https://doi.org/10.1016/j.asoc.2015.02.015
Jiang F, Zhu Z, Li W, Chen G, Zhou G (2013) Robust condition monitoring and fault diagnosis of rolling element bearings using improved EEMD and statistical features. Meas Sci Technol 25(2):025003. https://doi.org/10.1088/0957-0233/25/2/025003
Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE international conference on systems, man, and cybernetics. Computational cybernetics and simulation, vol 5, pp 4104–4108. IEEE. https://doi.org/10.1109/icsmc.1997.637339
Lei Y, Zuo MJ (2009) Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs. Meas Sci Technol 20(12):125701. https://doi.org/10.1088/0957-0233/20/12/125701
Lei Y, Liu Z, Ouazri J, Lin J (2017) A fault diagnosis method of rolling element bearings based on CEEMDAN. Proc Inst Mech Eng C 10:1804–1815. https://doi.org/10.1177/0954406215624126
Li Z, Yan X, Tian Z, Yuan C, Peng Z, Li L (2013) Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis. Measurement 46(1):259–271. https://doi.org/10.1016/j.measurement.2012.06.013
Li Y, Xu M, Wei Y, Huang W (2015) An improvement EMD method based on the optimized rational Hermite interpolation approach and its application to gear fault diagnosis. Measurement 63:330–345. https://doi.org/10.1016/j.measurement.2014.12.021
Lu L, Yan J, de Silva CW (2015) Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition. J Sound Vib 344:464–483. https://doi.org/10.1016/j.jsv.2015.01.037
Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693. https://doi.org/10.1109/34.192463
Nezamivand Chegini S, Bagheri A, Najafi F (2018) PSOSCALF: a new hybrid PSO based on sine cosine algorithm and Levy flight for solving optimization problems. Soft Comput, Appl. https://doi.org/10.1016/j.asoc.2018.09.019
Nezamivand Chegini S, Bagheri A, Najafi F (2019) Application of a new EWT-based denoising technique in bearing fault diagnosis. Measurement 144:275–297. https://doi.org/10.1016/j.measurement.2019.05.049
Nguyen P, Kang M, Kim JM, Ahn BH, Ha JM, Choi BK (2015) Robust condition monitoring of rolling element bearings using de-noising and envelope analysis with signal decomposition techniques. Expert Syst Appl 42(22):9024–9032. https://doi.org/10.1016/j.eswa.2015.07.064
Shan Y, Zhou J, Jiang W, Liu J, Xu Y, Zhao Y (2019) A fault diagnosis method for rotating machinery based on improved variational mode decomposition and a hybrid artificial sheep algorithm. Meas Sci Technol 30(5):055002
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: International conference on evolutionary computation proceedings. IEEE world congress on computational intelligence, pp 69–73. IEEE. https://doi.org/10.1109/ICEC.1998.699146
Song Q, Jiang H, Liu J (2017) Feature selection based on FDA and F-score for multi-class classification. Expert Syst Appl 81:22–27. https://doi.org/10.1016/j.eswa.2017.02.049
Sperduti A, Starita A (1997) Supervised neural networks for the classification of structures. IEEE Trans Neural Netw 8:714–735
Tabrizi A, Garibaldi L, Fasana A, Marchesiello S (2015) Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine. Meccanica 50(3):865–874. https://doi.org/10.1007/s11012-014-9968-z
Vakharia V, Gupta VK, Kankar PK (2016) Bearing fault diagnosis using feature ranking methods and fault identification algorithms. Proc Eng 144:343–350. https://doi.org/10.1016/j.proeng.2016.05.142
Vapnik V (1995) The nature of statistical learning theory. Springer, New York
Wang CS, Sha CY, Su M, Hu YK (2017) An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter. J Cent South Univ 24(2):478–488. https://doi.org/10.1007/s11771-017-3450-8
Wei Z, Wang Y, He S, Bao J (2017) A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection. Knowl Based Syst 116:1–12. https://doi.org/10.1016/j.knosys.2016.10.022
Xue X, Zhou J, Xu Y, Zhu W, Li C (2015) An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis. Mech Syst Signal Process 62:444–459. https://doi.org/10.1016/j.ymssp.2015.03.002
Yan X, Jia M (2018) A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing 313(3):47–64. https://doi.org/10.1016/j.neucom.2018.05.002
Yin H, Qiao J, Fu P, Xia XY (2014) Face feature selection with binary particle swarm optimization and support vector machine. J Inf Hiding Multimed Signal Process 5(4):731–739
Zeng Z, Zhang H, Zhang R, Yin C (2015) A novel feature selection method considering feature interaction. Pattern Recognit 48(8):2656–2666. https://doi.org/10.1016/j.patcog.2015.02.025
Zhang X, Chen W, Wang B, Chen X (2015) 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. https://doi.org/10.1016/j.neucom.2015.04.069
Zhang X, Zhang Q, Chen M, Sun Y, Qin X, Li H (2018) A two-stage feature selection and intelligent fault diagnosis method for rotating machinery using hybrid filter and wrapper method. Neurocomputing 275:2426–2439. https://doi.org/10.1016/j.neucom.2017.11.016
Zhao DZ, Li JY, Cheng WD, Wang TY, Wen WG (2016) Rolling element bearing instantaneous rotational frequency estimation based on EMD soft-thresholding denoising and instantaneous fault characteristic frequency. J Cent South Univ 23(7):1682–1689. https://doi.org/10.1007/s11771-016-3222-x
Ziani R, Felkaoui A, Zegadi R (2017a) 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. https://doi.org/10.1007/s10845-014-0987-3
Ziani R, Mahgoun H, Fedala S, Felkaoui A (2017b) Feature selection scheme based on Pareto method for gearbox fault diagnosis. Signal Process Appl Rotating Mach Diagn. https://doi.org/10.1007/978-3-319-96181-1_1
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This study was financially supported by the University of Guilan to S.N.C. The funder had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.
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S.N.C designed and coordinated the study. S.N.C wrote the manuscript. S.N.C, A.B, and F.N reviewed the manuscript and contributed to its revision. All the authors discussed the results and gave their final approval for publication.
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Nezamivand Chegini, S., Bagheri, A. & Najafi, F. A new intelligent fault diagnosis method for bearing in different speeds based on the FDAF-score algorithm, binary particle swarm optimization, and support vector machine. Soft Comput 24, 10005–10023 (2020). https://doi.org/10.1007/s00500-019-04516-z
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DOI: https://doi.org/10.1007/s00500-019-04516-z