Experimental studies for bearings degradation monitoring at an early stage using analysis of variance
,
 
,
 
 
 
More details
Hide details
1
Uinversité des Frères Mentouri Constantine 1
 
2
Laboratoire de Mécanique, Université des Frères Mentouri – Constantine 1 Department of Mechanical Engineering, Ecole de Technologie Supérieur
 
3
Department of Mechanical Engineering, Ecole de Technologie Supérieur
 
 
Submission date: 2018-04-24
 
 
Final revision date: 2018-07-17
 
 
Acceptance date: 2018-09-08
 
 
Online publication date: 2018-10-30
 
 
Publication date: 2018-11-05
 
 
Corresponding author
Salim Meziani   

Uinversité des Frères Mentouri Constantine 1, Laboratoire de Mécanique, Université des Frères Mentouri Constantine 1, 25000 Constantine, Algeria
 
 
Diagnostyka 2018;19(4):81-87
 
KEYWORDS
TOPICS
ABSTRACT
This work presents a procedure for bearing degradation monitoring at an early stage. The anal-ysis of variance (ANOVA) coupled with Tukey’s test is used to single out the suitable parameters to follow the fault size evolution ranging from 50 µm to 150µm. The Tukey's criterion is adopted in this case to study the ability of time and frequency indicators. The rotational speed, centrifu-gal load and fault size are considered as independent variables while the time and frequency in-dicators are taken as independent variables. The experiments are performed on bearings having a fault on outer race. Based on the results of this study, the Kurtosis and Skewness show a good ability to assess the evolution of degradation in the bearings at an early stage. The paper discuss-es the weakness of the time and frequency indicator
REFERENCES (27)
1.
Thomas M. Fiabilité, maintenance prédictive et vibrations de machines. Presses de l’Université du Québec 2011; 633: D335.
 
2.
Bazovsky. Reliability Theory and practice, Prentice-Hall, Englwood Cliffs, Nj 1961.
 
3.
Jones RM. A guide to the interpretation of machinery vibration measurements, Sound and Vibration, 1994; 28(9): 12-20.
 
4.
Tandon N, Choudhury A. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Journal of Tribology International, 1999; 32: 469-480.
 
5.
Randall RB, Antoni J. Rolling element bearing diagnostics, Mechanical Systems and Signal Processing. 2011; 25: 485–520.
 
6.
Sassi S, Badri B, Thomas M. Tracking surface degradation of ball bearings by means of new time domain scalar descriptors, International journal of COMADEM, 2008;11 (3): 36-45.
 
7.
Badri B, Thomas M, Sassi S. The envelop Shock detector: a new method to detect impulsive signals, International journal of COMADEM, 2012;15(3) :29-38.
 
8.
El Badaoui M. Contribution of vibratory diagnostic of gearbox by Cepstral analysis, Ph.D. thesis, Jean Monnet University of St Etienne (FR), 1999: 141.
 
9.
Berry J. How to track rolling bearing health with vibration signature analysis, Sound and Vibration. 1991: 24-35.
 
10.
De Priego J.C.M. The relationship between vibration spectra and spike energy spectra for an electric motor bearing defect, Vibrations. 2001; 17(1):3-5.
 
11.
Altmann J, Mathew J. Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis, Mechanical Systems and Signal Processing. 2001; 15(5):963–977.
 
12.
Kedadouche M, Thomas M, Tahan A. Monitoring bearing defects by using a method combining EMD, MED and TKEO. Advances in Acoustics and Vibration. Hindawi Publishing Corporation, 2014, ID 502080, 10. http://dx.doi.org/10.1155/2014....
 
13.
Kedadouche M, Thomas M, Tahan A. Cyclostationarity applied to acoustic emission and development of a new indicator for monitoring bearing defects. Mechanics & Industry, 2014; 5(6): 467–476. https://doi.org/10.1051/meca/2....
 
14.
Antoni J, Bonnardot F, Raada A, El Badaoui M. Cyclostationary modelling of rotating machine vibration signals, Mechanical Systems and Signal Processing. 2004; 18(6):1285–1314.
 
15.
Safizadeh M.S, Lakis AA, Thomas M. Time-frequency distributions and their application to machinery fault detection. International Journal of Condition Monitoring and Diagnosis Engineering Management, 2002;5(2):41-56.
 
16.
Braun S, Feldman M. Time-frequency characteristics of non-linear systems. Mechanical Systems and Sig-nal Processing. 1997; 11(4):611-620.
 
17.
Safizadeh MS, Lakis AA, Thomas M. Using Short Time Fourier Transform in Machinery Fault Diagnosis, International Journal of Condition Monitoring and Diagnosis Engineering Management (COMADEM), 2000;3(1): 5-16.
 
18.
Tse P, Peng YH, Yam R. Wavelet analysis and envelope detection for rolling element bearing fault diagnosis—their effectiveness and flexibility, Transactions of the ASME, Journal of Vibration and Acoustics. 2001; 123(3):303–310.
 
19.
Antoni J, Randall RB. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines, Mechanical Systems and Signal Processing. 2006;20:308–331.
 
20.
Batista L, Badri B, Sabourin R, Thomas M. A classifier fusion system for bearing fault diagnosis, expert systems with applications. Elsevier. 2013;40 (17):6788-6797.
 
21.
Gonsalez CG, da Silva S, Brennan MJ. Structural damage detection in an aeronautical panel using analysis of variance. Mechanical Systems and Signal Processing. 2015;52-53:06–216. https://doi.org/10.1016/j.ymss....
 
22.
Aouici H, Yallese MA, Fnides B, Chaoui K, Mabrouki T. Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool: Machining parameters effects on flank wear and surface roughness. Journal of Mechanical Science and Technology. 2011;25(11): 2843-2851.
 
23.
Ram Prabhu T. Effects of solid lubricants, load, and sliding speed on the tribological behavior of silica reinforced composites using design of experiments. Materials and Design. 2015; 77:149–160. https://doi.org/10.1016/j.matd....
 
24.
Hochberg Y, Tamhane AC. Multiple comparison procedures. Willey, Canada, 1987.
 
25.
Conover WJ. Practical nonparametric statistics, Wiley, New York, 1980.
 
26.
Lilliefors HW. On the Kolmogorov-Smirnov test for normality with mean and variance un-known. Journal of the American Statistical Association. 1967; 62: 399–402.
 
27.
Hogg RV, Ledolter J. Engineering statistics. Mac Millan Publishing Company, Universidad de Michigan, 1987.
 
eISSN:2449-5220
Journals System - logo
Scroll to top