Complex Morlet wavelet design with global parameter optimization for diagnosis of industrial manufacturing faults of tapered roller bearing in noisycondition
More details
Hide details
University of Debrecen, Faculty of Engineering
Submission date: 2019-01-17
Final revision date: 2019-05-07
Acceptance date: 2019-05-08
Online publication date: 2019-05-11
Publication date: 2019-05-11
Corresponding author
Deák Krisztián Deák   

University of Debrecen, Faculty of Engineering
Diagnostyka 2019;20(2):77-86
Detecting manufacturing defects of bearings are difficult because of their unique topography. To find adequate methods for diagnosis is important because they could be responsible for serious problems. Wavelet transform is an efficient tool for analyzing the transients in the vibration signal. In this article we are focusing on industrial grinding faults on the outer ring of tapered roller bearings. Nine different real-valued wavelets, Symlet-2, Symlet-5, Symlet-8, Daubechies (2, 6, 10, 14), Morlet and Meyer wavelets are compared to a designed complex Morlet wavelet according to the Energy-to-Shannon-Entropy ratio criteria to determine which is the most efficient for detecting the manufacturing fault. Parameters of the complex Morlet wavelet are adjustable, thus, it has more flexibility for feature extraction. Genetic algorithm is applied to optimize the center frequency and the bandwidth of the designed wavelet. A sophisticated filtering procedure through multi-resolution analysis is applied with autocorrelation enhancement and envelope detection. To determine the efficiency of the designed wavelet and compare to the other wavelets, a test-rig was constructed equipped with high-precision sensors and devices. The designed wavelet is found to be the most effective to detect the manufacturing fault. Therefore, it has the capacity for an industrial testing procedure.
Sawalhi N, Randall RB. Vibration response of spalled rolling element bearings: observations, simulations and signal processing techniques to track the spall size. Mechanical Systems and Signal Processing. 2011; 25:846.
Prabhakar S, Mohanty AR, Sekhar AS. Application of discrete wavelet transform for detection of ball bearing race faults. Tribology International. 2002; 35:793.
Shi DF, Wang WJ, Qu LS. Defect detection for bearings using envelope spectra of wavelet transform. ASME Journal of Vibration and Acoustics. 2004; 126:567.
Nikolaou NG, Antoniadis IA. Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets. Mechanical Systems and Signal Processing. 2002; 16:677.
Kumar R, Singh M. Outer race defect width measurement in tapered roller bearing using discrete wavelet transform of vibration signal. Measurement. 2013; 46:537.
Kumar R, Jena DP, Bains M. Identification of inner race defect in radial ball bearing using acoustic emission and wavelet analysis. Proceedings of ISMA 2010 including USD 2010 Leuven (Belgium). 2010; 2883–2891.
He W, Jiang Z, Feng K. Bearing fault detection based on optimal wavelet filter and sparse code shrinkage. Measurement. 2009; 42:1092–1102.
NI 9234 datasheet:, accessed on 2016-02-04 .
PCB IMI 603C01 transducer, from, accessed on 2016-02-04.
Misiti M, Misiti Y, Oppenheim G, Poggi JM. Wavelets and their Applications. 2007; ISTE Ltd.
Honghu P, Xingxi H, Sai T, Fanming M. An improved bearing fault diagnosis method using one-dimensional CNN and LSTM. Strojniski vestnik-Journal of Mechanical Engineering. 2018; 64:443-452.
Ivan O, Marko N, Jernej K. Analysis on damage to rolling bearings at small turning angles. Strojniski vestnik-Journal of Mechanical Engineering. 2018; 64: 209-215.
Kankar PK, Sharma SC, Harsha SP. Fault diagnosis of ball bearings using continuous wavelet transform. Applied Soft Computing. 2011; 11:2300–2312.
Harsha SP, Kankar PK, Sharma SC. Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform. Neurocomputing. 2013; 110:9-17.
Li Z, Ma Z, Liu Y, Teng W, Jiang R. Crack fault detection for a gearbox using discrete wavelet transform and an adaptive resonance theory neural network. Strojniski vestnik-Journal of Mechanical Engineering. 2015; 61:63-73.
Patel VN, Tandon N, Pandey RK. Defect detection in deep groove ball bearing in presence of external vibration using envelope analysis and Duffing oscillator. Measurement. 2012; 45:960-970.
Tabaszewski, M. Optimization of a nearest neighbors classifier for diagnosis of condition of rolling bearings. Diagnostyka. 2014; 15: 37-42.
Strączkiewicz M, Czop P, Barszcz T. Supervised and unsupervised learning process in damage classification of rolling element bearings. Diagnostyka. 2016; 17:71-80.
Wensheng S, Fengtao W, Hong Z, Zhixin Z, Zhenggang G. Rolling element bearing fault diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement. Mechanical Systems and Signal Processing. 2009; 24:1458-1472.
Jena DP, Panigrahi S, Kumar R. Gear fault identification and localization using analytic wavelet transform of vibration signal. Measurement, 2013; 46:1115-1124.
Tandon N, Choudhury A. A review of vibration and acoustic measurement methods for detection of defects in rolling element bearings. Tribology International. 1999; 32:469-480.
Farzad H, Wasim O, Mohamed S. Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation. Applied acoustics. 2016; 104:101-118.
Deak K, Kocsis I, Mankovits T. Optimal Wavelet Selection for Manufacturing Defect Size Estimation of Tapered Roller Bearings with Vibration Measurement using Shannon Entropy Criteria. Strojniški vestnik - Journal of Mechanical Engineering. 2017; 63:3-14.
Journals System - logo
Scroll to top