Use of the Correlation Coefficient for Rotating Machine Monitoring
Journal of Engineering Research and Reports,
Page 30-37
DOI:
10.9734/jerr/2021/v21i817485
Abstract
The use of electronics and computer technology in production systems has greatly improved the quality of our industrial products. The productivity of these installations is a function of the maintenance quality applied to the equipment. Several methods are used to monitor the functioning of industrial installations. One of these methods is vibration analysis. The vibration signals from the rotating machines support several types of information related to the working state of the production tool. The processing of this information makes it possible to have decision tools for maintenance. In this work, we propose a method of anticipating the maintenance of rotating machines. The algorithm we propose starts with the removal of 512 point windows during the running time of the ball bearing. Each signal is decomposed by DWT: we obtain the approximation coefficients. These coefficients make it possible to determine the correlation coefficient between the so-called reference window and the other windows following the functioning of the ball bearing. The correlation coefficient is then the fundamental element of the decision. This algorithm has been applied to real vibration data and the results are encouraging.
Keywords:
- DWT
- correlation coefficient
- vibration signals
- rotating machines
How to Cite
References
1 Merzoug M, Ait-Sghir K, Miloudi A, Dron JP, Bolaers F. Early Detection of Gear Failure by Vibration Analysis. In: Haddar M. et al. (eds) Multiphysics Modelling and Simulation for Systems Design and Monitoring. MMSSD 2014. Applied Condition Monitoring. Springer, Cham. Available:https://doi.org/10.1007/978-3-319-14532-7_8
Wang WJ, McFadden PD. Early Detection of Gear Failure by Vibration Analysis--I. Calculation of the Time Frequency Distribution. Mechanical Systems and Signal Processing, 1993;7(3):193-203.
ISSN 08883270,
Avaialble:http://dx.doi.org/10.1006/mssp.1993.1009
Oulmane A, Lakis AA, Mureithi N. "Application of Fourier Descriptors & Artificial Neural Network to Bearing Vibration Signals for Fault Detection & Classification", Universal Journal of Aeronautical & Aerospace Sciences. 2014;2:37-54
Avaialble:www.papersciences.com/
Bo Li, Mo-Yuen Chow, Yodyium Tipsuwan and James C. Hung "Neural-Network-Based Motor Rolling Bearing Fault Diagnosis" IEEE Transactions On Industrial Electronics. 2000;47(5):1060-1069
Minamihara H, Nishimura M, Takakuwa Y, Ohta M. A method of detection of the correlation function and frequency power spectrum for random noise or vibration with amplitude limitation, Journal of Sound Vibration. 1990;141(3): 425-434
Rafiee J, Rafiee MA, Prause N, Tse PW. Automatic frequency extraction using sinusoidal approximation and wavelet transform; 2009.
DOI: 10.1109/IC4.2009.4909248
Peng ZK, Tse PW, Chu FL. A comparison study of improved Hilbert–Huang transform and wavelet transform: application to fault diagnosis for rolling bearing”, Mechanical Systems and Signal Processing. 2005;19 (5):974–988.
Ma Yuchao, Yan Weiming, He Haoxiang, Wang Kai, "Damage Detection Based on Cross-Term Extraction from Bilinear Time-Frequency Distributions", Mathematical Problems in Engineering, 2014;2014:10 Article ID 986050, .
Avaialble:https://doi.org/10.1155/2014/986050
Peng ZK, Chu FL. Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography Mechanical Systems and Signal Processing». 200;18(2)4: 199-221.
Eduardo Rubio, Guillermo Ramírez ‘A wavelet approach to detect gear-rattle development in mechanical systems’ Journal of Vibroengineering. 2015;17(5):2283-2290.
Chaoang X, Hesheng T, Yan R. Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model. Measurement and Control. 2020;53(3-4):649–661.
Available:https://dx.doi.org/10.1177/0020294019898725.
Aimé Joseph Oyobe Okassa, Jean Pierre Ngantcha, Auguste Ndtoungou and Pierre ELE, Use of Lazy Wavelet and DCT for Vibration Signal Compression, American Journal of Engineering and Applied Sciences. 2021; 14 (1): 1.6
DOI: 10.3844/ajeassp.2021.1.6
Xiao Chaoang, Tang Hesheng and Ren Yan "Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary" Measurement and Control . 2020;53(3-4):649–661.
DOI: 10.1177/00202940198;
Narayan S, Sasa Milojevic, Vipul Gupta “Combustion monitoring in engines using accelerometer signals”. Journal of Vibroengineering. 2019;21(6),:1552-1563. Avaialble:https://doi.org/10.21595/jve.2019.20516
Combet F, Gelman L, Anuzis P, Slater R.Vibration detection of local gear damage by advanced demodulation and residual techniques. Proc. IMechE, Part G: J. Aerospace Engineering. 2009;223:507-514.
Howard C. Mahler et Curtis Gary Dean, Foundations of Casualty Actuarial Science, Casualty Actuarial Society. 2001:525–526
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