Abstract
This paper is focused on comparison of effectiveness of artificial intelligence (AI) techniques in fault diagnosis of rolling element bearings. The features for classification are extracted through wavelet packet decomposition using RBIO 5.5 wavelet. The whole classification is done using two features: energy and Kurtosis. The data samples for classification are taken with reference to a healthy bearing, thus, minimizing the errors from the experimental set-up. Four bearing conditions such as bearing with outer race defect, inner race defect, ball defect and combined defect on outer race, inner race and ball have been used in this paper. Localized defects of micron level are induced through laser machining. The effectiveness of three AI techniques viz. ANN, SVM and multinomial logistic regression are compared. The results show that the Logistic Regression technique is the more effective than other two techniques as ANN and SVM.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Chaudhuri BB, Bhattacharya U (2000) Efficient training and improved performance of multilayer perceptron in pattern classification. Neurocomputing 34:11–27
Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28:337–407
Hall MA (1999) Correlation-based feature selection for machine learning. Dissertation. The University of Waikato, Hamilton
Hall M, Frank E, Geoffrey H, Bernhard P, Peter R, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1)
Kankar PK, Sharma SC, Harsha SP (2011a) Fault diagnosis of ball bearing using continuous wavelet transform. Appl Soft Comput 11:2300–2312
Kankar PK, Sharma SC, Harsha SP (2011b) Fault diagnosis of ball bearings using machine learning methods. Expert Syst Appl 38(3):1876–1886
Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK (2001) Improvements to Platt’s SMO algorithm for SVM classifier design. Neural Comput 13:637–649
Le CS, Houwelingen JC (1992) Ridge estimators in logistic regression. Appl Stat 41(1):191–201
Lei Y, He Z, Zi Y (2009) Application of an intelligent classification method to mechanical fault diagnosis. Expert Syst Appl 36:9941–9948
Lin J, Qu L (2000) Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. JSV 234(1):135–148
Mingjing Y, Huiru Z, Haiying W, Sally M (2009) Feature selection and construction for the discrimination of neurodegenerative diseases based on gait analysis. http://dx.doi.org/10.4108/ICST.PERVAS/VEHEALTH2009.6053
Nikolaou NG, Antoniadis IA (2002) Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets. MSSP 16(4):677–694
Platt J (1998) Advances in kernel methods: fast training of support vector machines using sequential minimal optimization. MIT Press, Cambridge
Prabhakar S, Mohanty AR, Sekhar AS (2002) Application of discrete wavelet transform for detection of ball bearing race faults. Tribol Int 35:793–800
Purushothama V, Narayanana S, Suryanarayana ANP (2005) Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden markov model based fault recognition. NDT E Int 38:654–664
Rafiee J, Arvani F, Harifi A, Sadeghi MH (2007) Intelligent condition monitoring of a gearbox using artificial neural network. MSSP 21:1746–1754
Rafiee J, Tse PW, Harifi A, Sadeghi MH (2009) A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system. Expert Syst Appl 36:4862–4875
Rafiee J, Rafiee MA, Tse PW (2010) Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Syst Appl 37:4568–4579
Samanta B, Al-Balushi KR (2003) Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. MSSP 17(2):317–328
Samanta B, Nataraj C (2009) Use of particle swarm optimization for machinery fault detection. Eng Appl Artif Intell 22(2):308–316
Saravanan N, Siddabattuni VNSK, Ramachandran KI (2008) A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis. Expert Syst Appl 35:1351–1366
Sugumaran V, Ramachandran KI (2011) Effect of number of features on classification of roller bearing faults using SVM and PSVM. Expert Syst Appl 38:4088–4096
Wu TY, Chen JC, Wang CC (2012) Characterization of gear faults in variable rotating speed using Hilbert–Huang Transform and instantaneous dimensionless frequency normalization. MSSP 30:103–122
Yang BS, Han T, Hwang WW (2005) Fault diagnosis of rotating machinery based on multi-class support vector machines. J Mech Sci Technol 19(3):846–859
Yilmaz I, Kaynar O (2011) Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 38:5958–5966
Yuan SF, Chu FL (2007a) Fault diagnosis based on support vector machines with parameter optimization by artificial immunization algorithm. MSSP 21(3):1318–1330
Yuan SF, Chu FL (2007b) Fault diagnosis based on particle optimization and support vector machines. MSSP 21(4):1787–1798
Zhongsheng H, Wang Yu, Xu X, Zhang B, Liang L (2007) Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Syst Appl 33:434–440
Acknowledgments
This work is financially supported by the Department of Science and Technology, Government of India (Grant number DST/457/MID).
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by D. Liu.
Rights and permissions
About this article
Cite this article
Pandya, D.H., Upadhyay, S.H. & Harsha, S.P. Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft Comput 18, 255–266 (2014). https://doi.org/10.1007/s00500-013-1055-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-013-1055-1