Abstract
The bearing element is an essential part in different mechanical equipment and rotating machinery. An unexpected failure of the bearing may cause substantial economic losses. Vibration analysis has been used as one of the most preferred measured signal to detect the bearing related fault. In this paper, an online method for automatic rolling-element bearing fault diagnosis using Multiclass Support Vector Machine MSVM is proposed. Firstly, the Empirical Mode Decomposition EMD technique is used to decompose the vibration signal into a finite number of stationary intrinsic mode functions (IMFs), then different parameters are calculated based FFT algorithm. The frequency analysis results of different EMD intrinsic mode functions for different vibration signals show that these intrinsic mode functions will change in different frequency bands when a bearing fault occurs. Therefore, to identify roller bearing fault class, the Locality Sensitivity Discriminant Analysis LSDA algorithm is used to transform the selected feature to low-dimensional space. The final features could serve as input vectors of trained MSVM. The analysis results from roller bearing signals with inner-race, out-race and ball faults show that the proposed diagnostic approach based on the MSVM as a classifier and the EMD as a signal processing technique to extract different stationary intrinsic mode functions as features can identify roller bearing fault type and severity patterns accurately and effectively even the analyzed vibration signal is acquired in the case of varying load and speed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Attoui I, Omeiri A (2014) Contribution to the fault diagnosis of a doubly fed induction generator for a closed-loop controlled wind turbine system associated with a two-level energy storage system. Electr Power Compon Syst 42(15):1727–1742
Boudiaf A, Moussaoui A, Dahane A, Atoui I (2016) A comparative study of various methods of bearing faults diagnosis using the Case Western Reserve University data. J Fail Anal Prev 16(2):271–284
Attoui I, Omeiri A (2015) Fault diagnosis of an induction generator in a wind energy conversion system using signal processing techniques. Electr Power Compon Syst 43(20):2262–2275
Thorsen OV, Dalva M (1999) Failure identification and analysis for high-voltage induction motors in the petrochemical industry. IEEE Trans Ind Appl 35(4):810–818
Motor Reliability Working Group (1985) Report of large motor reliability survey of industrial and commercial installations, Part I. IEEE Trans Ind Appl IA-21(4):853–864
Garcia-Perez A, Romero-Troncoso RDJ, Cabal-Yepez E, Osornio-Rios RA (2011) The application of high-resolution spectral analysis for identifying multiple combined faults in induction motors. IEEE Trans. Ind Electron 58(5):2002–2010
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
Lau ECC, Ngan HW (2010) Detection of motor bearing outer raceway defect by wavelet packet transformed motor current signature analysis. IEEE Trans Instrum Meas 59(10):2683–2690
Sperduti A, Starita A (1997) Supervised neural networks for the classification of structures. IEEE Trans Neural Netw
Attoui I, Boutasseta N, Fergani N, Oudjani B, Deliou A (2015) Vibration-based bearing fault diagnosis by an integrated DWT-FFT approach and an adaptive neuro-fuzzy inference system. In: 3rd international conference on control, engineering and information technology, CEIT 2015
Attoui I, Fergani N, Oudjani B, Deliou A (2017) Rolling bearing fault diagnosis based on improved complete ensemble empirical mode decomposition. In: 4th international conference on control engineering and information technology, CEIT 2016
Atoui I, Meradi H, Boulkroune R, Saidi R, Grid A (2013) Fault detection and diagnosis in rotating machinery by vibration monitoring using FFT and Wavelet techniques. In: 2013 8th international workshop on systems, signal processing and their applications (WoSSPA), pp 401–406
Attoui I, Boudiaf A, Fergani N, Oudjani B, Boutasseta N, Deliou A (2015) Vibration-based gearbox fault diagnosis by DWPT and PCA approaches and an adaptive neuro-fuzzy inference system. In: 2015 16th international conference on sciences and techniques of automatic control and computer engineering (STA), pp 234–239
Sain SR, Vapnik VN (1996) The nature of statistical learning theory. Technometrics 38(4):409
Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Pincus SM (1991) Approximate entropy as a measure of system complexity. Mathematics 88:2297–2301
Zhang L, Xiong G, Liu H, Zou H, Guo W (2010) Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Syst Appl 37(8):6077–6085
Lempel A, Ziv J (1976) On the complexity of finite sequences. IEEE Trans Inf Theory 22(1):75–81
Yan R, Gao RX (2011) An introduction to complexity measure: Non-linear statistical parameters in measurements: Part 35 in a series of tutorials on instrumentation and measurement. IEEE Instrum Meas Mag 14(5):27–35
Soualhi A, Medjaher K, Zerhouni N (2015) Bearing health monitoring based on hilbert-huang transform, support vector machine, and regression. IEEE Trans Instrum Meas
Goumas SK, Zervakis ME, Stavrakakis GS (2002) Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction. IEEE Trans Instrum Meas
Zheng J, Cheng J, Yang Y (2013) A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy. Mech Mach Theory
Kang M, Kim J, Kim J-M (2015) Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm. Inf Sci (Ny) 294:423–438
Boškoski P, Gašperin M, Petelin D, Juričić D (2015) Bearing fault prognostics using Rényi entropy based features and Gaussian process models. Mech Syst Signal Process
He D, Li R, Zhu J (2013) Plastic bearing fault diagnosis based on a two-step data mining approach. IEEE Trans Ind Electron 60(8):3429–3440
Seshadrinath J, Singh B, Panigrahi BK (2014) Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets. IEEE Trans Power Electron 29(2):936–945
C. W. R. University (2015). http://csegroups.case.edu/bearingdatacenter/pages/download-datafile
Shalev-Shwartz S, Singer Y, Srebro N, Cotter A (2011) Pegasos: primal estimated sub-gradient solver for SVM. Math Program
Hsieh CJ, Chang KW, Lin CJ, Keerthi SS, Sundararajan S (2008) A dual coordinate descent method for large-scale linear SVM. In: Proceedings of the 25th international conference on machine learning
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Attoui, I., Fergani, N., Boutasseta, N., Oudjani, B., Bouakkaz, M.S., Bouraiou, A. (2021). Multiclass Support Vector Machine Based Bearing Fault Detection Using Vibration Signal Analysis. In: Bououden, S., Chadli, M., Ziani, S., Zelinka, I. (eds) Proceedings of the 4th International Conference on Electrical Engineering and Control Applications. ICEECA 2019. Lecture Notes in Electrical Engineering, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-15-6403-1_61
Download citation
DOI: https://doi.org/10.1007/978-981-15-6403-1_61
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6402-4
Online ISBN: 978-981-15-6403-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)