Abstract
In the detection of bearing faults the so much desired objective remains the extraction of the defect vibratory signature from the measured signal in which immerses the random noise and other components of the machine. In this article a denoising method of the measured signals is presented. Based on the optimization of wavelet multiresolution analysis, it uses the kurtosis as an optimization and evaluation criterion, several parameters were then selected. The experimental results show the validity of this method within the detection of several defects simulated on ball bearings. The various configurations, in which the signals were measured, allow leading to optimum conditions of its application. The application of WMRA on filtered signals allows better results than its application on wide bands signals or a simple band pass filtering.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Tandon N, Choudhury A (1999) A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribol Int 32:469–480
Dyer D, Stewart M (1978) Detection of rolling element bearing damage by statistical vibration analysis. J Mech Des 100:229–235
Boulenger A, Pachaud C (1998) Diagnostic vibratoire en maintenance préventive. Dunod, Paris
Pachaud C, Salvetas R, Fray C (1997) Crest factor and kurtosis contributions to identify defects inducing periodical impulsive forces. Mech Syst Signal Process 11(6):903–916
Heng RBW, Nor MJ (1998) Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition. Appl Acoust 53(1–3):211–226
MacFadden PD, Smith JD (1984) Vibration monitoring of rolling element bearing by the high frequency resonance technique, a review. Tribol Int 17(1):3–10
Chaturvedi GK, Thomas DW (1982) Bearings faults detection using adaptive noise cancelling. J Mech Des 104:280–289
Khemili I, Chouchane M (2005) Detection of rolling element bearing defects by adaptive filtering. Eur J Mech A: Solids 24:293–303
Dron JP, Bolaers F, Rasolofondraibe L (2003) Optimization de la détection des défauts de roulements par débruitage des signaux par soustraction spectrale. Mec Ind 4:213–219
Dron JP, Bolaers F, Rasolofondraibe L (2004) Improvement of the sensitivity of the scalar indicators (crest factor and kurtosis) using a de-noising method by spectral subtraction: application to the detection of defects in ball bearings. J Sound Vib 270:61–73
Bolaers F, Cousinard O, Marconnet P, Rasolofondraibe L (2004) Advanced detection of rolling bearing spalling from de-noising vibratory signals. Control Eng Pract 12:181–190
Donoho DL (1995) De-noising by soft thresholding. IEEE Trans Inf Theory 41(3):613–627
Qiu H, Lee J, Lin J, Yu G (2006) Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J Sound Vib 289:1066–1090
Shao Y, Nezu K (2004) Design of mixture de-noising for detecting faulty bearing signals. J Sound Vib 282:899–917
Lin J (2000) Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. J Sound Vib 234(1):35–148
Peter W (2000) Wavelets analysis-A flexible and efficient fault diagnostic method for rolling element bearing. In: 7th international congress on sound and vibration, Germany, 4–7 July 2000, pp 507–514
Sun Q, Tang Y (2002) Singularity analysis using continuous wavelet transform for bearing fault diagnosis. Mech Syst Signal Process 16(6):1025–1041
Rubini R, Meneghetti U (2001) Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mech Syst Signal Process 15(2):287–302
Boltezar M, Simonovski I, Furlan M (2003) Faults detection in DC electro motors using the continuous wavelet transform. Meccanica 38:251–264
Yang DM, Stronach AF, MacConnell P (2003) The application of advanced signal processing techniques to induction motor bearing condition diagnosis. Meccanica 38:297–308
Brabhakar S, Mohanty AR, Sekhar AS (2002) Application of discrete wavelet transform for detection of ball bearings race faults. Tribol Int 35:793–800
Nikolaou NG, Antoniadis IA (2002) Rolling element bearing fault diagnosis using wavelet packets. NDT & E Int 35:197–205
Mori K, Kasashima N, Yoshioda T, Ueno Y (1996) Prediction of spalling on a ball bearing by applying discrete wavelet transform to vibration signals. Wear 8:195–162
Li JC, Jun M (1997) Wavelet decomposition of vibrations for detection of bearing-localized defects. NDT & E Int 30(3):143–149
Chinmaya K, Mohanty AR (2006) Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mech Syst Signal Process 20(2):158–187
Wang WJ, MacFadden PD (1996) Application of wavelets to gearbox vibration signals for fault detection. J Sound Vib 192(5):927–939
Zheng H, Li Z, Chen X (2002) Gear faults diagnosis based on continuous wavelet transform. Mech Syst Signal Process 16(2–3):447–457
Yoshida A, Ohue Y, Ishikawa H (2000) Diagnosis of tooth surface failure by wavelet transform of dynamic characteristics. Tribol Int 33:273–279
Sung CK, Tai HM, Chen CW (2000) Locating defects of gear system by the technique of wavelet transform. Mech Mach Theory 35:1169–1182
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Djebala, A., Ouelaa, N. & Hamzaoui, N. Detection of rolling bearing defects using discrete wavelet analysis. Meccanica 43, 339–348 (2008). https://doi.org/10.1007/s11012-007-9098-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11012-007-9098-y