Exploring an effective assessment index is significant for performance degradation assessment, which has been proposed to realize equipments’ near-zero downtime and maximum productivity. In this paper, the Kolmogorov-Smirnov test based on an autoregressive model is proposed to assess the performance degradation of rolling bearings. Accelerated life test (in Hangzhou Bearing Test and Research Center) of rolling bearings was performed to collect vibration data over a whole lifetime (normal-fault-failure). The result shows that the Kolmogorov-Smirnov test method can obviously detect incipient weak defects and can reflect performance degradation process well. In particular, it can detect abnormal stages earlier before the bearing steps into failure, which is significant in condition maintenance and prognosis.
Akaike H. ( 1969) Fitting autoregressive for prediction. Annals of the Institute of Statistical Mathematics21: 243-247.
2.
Akaike H. ( 1974) A new look at the statistical model identification . IEEE Transactions on Automatic Control19: 716-723.
3.
Andrade FA, Esat I. and Badi MNM ( 2001) A new approach to time-domain vibration condition monitoring: gear tooth fatigue crack detection and identification by the Kolmogorov-Smirnov test. Journal of Sound and Vibration240(5): 909-919.
4.
Antoni J. ( 2006) The spectral kurtosis: a useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing20(2): 282-307.
5.
Antoni J. ( 2007) Cyclic spectral analysis of rolling-element bearing signals: facts and fictions. Journal of Sound and Vibration304(3-5): 497-529.
6.
Antoni J. and Randall RB ( 2002) Differential diagnosis of gear and bearing faults . ASME Journal of Vibration and Acoustics124(2): 165-171.
7.
Antoni J. and Randall RB ( 2004) Unsupervised noise cancellation for vibration signals: Part I - evaluation of adaptive algorithms. Mechanical Systems and Signal Processing18(1): 89-101.
8.
Antoni J. and Randall RB ( 2006) The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing20(2): 308-331.
9.
Bannister RH ( 1985) A review of rolling element bearing monitoring techniques . In: Condition Monitoring of Machinery Seminar , London, England, 6 June 1985.
10.
Dyer D. and Stewart RM ( 1978) Detection of rolling element bearing damage by statistical vibration analysis. Journal of Mechanical Design100: 229-235.
11.
Fodor IK and Berryman JG ( 1997) Comparison of autoregressive and multitaper spectral analysis for long time series. Stanford Exploration Project, Report95: 331-356.
12.
Ho D. and Randall RB ( 2000) Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechanical Systems and Signal Processing14(5): 763-788.
13.
Honarvar F. and Martin HR ( 1997) New statistical moments for diagnostics of rolling element bearings. ASME Journal of Manufacturing Science and Engineering119(3): 425-432.
14.
Jardine AKS, Lin D. and Banjevic D. ( 2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing20(7): 1483-1510.
15.
Junsheng C., Dejie Y. and Yu Y. ( 2006) A fault diagnosis approach for roller bearings based on EMD method and AR model. Mechanical Systems and Signal Processing20(2): 350-362.
16.
Kar C. and Mohanty AR ( 2004) Application of KS test in ball bearing fault diagnosis . Journal of Sound and Vibration269(1-2): 439-454.
17.
Lee J. ( 1996) Measurement of machine performance degradation using a neural network model. Computers in Industry30(3): 193-209.
18.
McFadden PD and Smith JD ( 1984a) Model for the vibration produced by a single point defect in a rolling element bearing. Journal of Sound and Vibration96(1): 69-82.
19.
McFadden PD and Smith JD ( 1984b) Vibration monitoring of rolling element bearings by the high-frequency resonance technique - a review. Tribology International17(1): 3-10.
20.
McFadden PD and Smith JD ( 1985) The vibration produced by multiple point defects in a rolling element bearing. Journal of Sound and Vibration98(2): 263-273.
21.
Martin KF ( 1994) A review by discussion of condition monitoring and fault-diagnosis in machine-tools. International Journal of Machine Tools and Manufacture34: 527-551.
22.
Martin HR and Honarvar F. ( 1995) Application of statistical moments to bearing failure detection. Applied Acoustics44: 67-77.
23.
Miller LH ( 1956) Table of percentage points of Kolmogorov statistics . JSTOR51: 111-121.
24.
Mitchell JS ( 1999) The history of condition monitoring and condition based maintenance. Journal of Sound and Vibration33(11): 21-28.
25.
Nikolaou NG and Antoniadis IA ( 2002) Rolling element bearing fault diagnosis using wavelet packets. NDT & E International35(3): 197-205.
26.
Ocak H. and Loparo KA ( 2005) HMM-based fault detection and diagnosis scheme for rolling element bearings. ASME Journal of Vibration and Acoustics127(4): 299-306.
27.
Ocak H., Loparo KA and Discenzo FM ( 2007) Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: a method for bearing prognostics . Journal of Sound and Vibration302(4-5): 951-961.
28.
Pan YN,Chen J. and Li XL ( 2009) Spectral entropy: a complementary index for rolling element bearing performance degradation assessment. In Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science223(5): 1223-1231.
29.
Randall RB, Antoni J. and Chobsaard S. ( 2001) The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mechanical Systems and Signal Processing15(5): 945-962.
30.
Samanta B., Al-Balushi KR and Al-Araimi SA ( 2006) Artificial neural networks and genetic algorithm for bearing fault detection. Soft Computing10(3): 264-271.
31.
Sawalhi N. ( 2007) Diagnostics, prognostics and fault simulation for rolling element bearings. PhD Dissertation, University of New South Wales, Sydney, Australia.
32.
Shigley JE, Mischke CRM and Budynas RG ( 2004) Mechanical Engineering Design, 7th edn. New York: McGraw-Hill.
33.
Sunnersjo CS ( 1985) Rolling bearing vibrations - the effects of geometrical imperfections and wear. Journal of Sound and Vibration98(4): 455-474.
34.
Tandon N. and Choudhury A. ( 1999) A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International32(8): 469-480.
35.
Wang W. and Wong AK ( 2002) Autoregressive model-based gear fault diagnosis . ASME Journal of Vibration and Acoustics124(2): 172-179.
36.
Wang X. and Makis V.(2009) Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov-Smirnov test. Journal of Sound and Vibration327(3-5): 413-423.
37.
Widodo A. and Yang BS ( 2007) Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing21(6): 2560-2574.
38.
Yu Y., Yu D. and Junsheng C. ( 2006) A roller bearing fault diagnosis method based on EMD energy entropy and ANN. Journal of Sound and Vibration294(1-2): 269-277.
39.
Zhan YM, Makis V. and Jardine Andrew KS ( 2006) Adaptive state detection of gearboxes under varying load conditions based on parametric modelling. Mechanical Systems and Signal Processing20(1): 188-221.
