A Low Frequency Uni-variate Model for the Effective Diagnosis and Prognosis of Bearing Signals Based Upon High Frequency Data

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Jamie L. Godwin Peter Matthews

Abstract

Prognosis of rotating machinery is of vital importance to ensure ever increasing demands of availability, reduced maintenance expenditure and increased useful life are met. However, the prognosis of bearings typically employs techniques in the frequency or time-frequency domain due to the high frequency nature of the data involved (typically >20 KHz). This data quickly becomes unmanageable in practice and often has inferior prognostic horizons in comparison to those techniques which are based upon low frequency data analysis.This paper presents a novel methodology based upon the computation of the deviation from the empirically derived cumulative density function (CDF) of bearing data. For this purpose, the non-parametric, two sample, uni-variate Kolmogorov-Smirnov test is employed for the analysis. In particular, this paper focuses on mitigating the requirement of a-priori knowledge for bearing prognosis.Initially, assumptions regarding the underlying structure of high frequency bearing data are explored on publically available data, and found to deviate from what would be expected.Exploiting this, we use the non-parametric two-sample uni- variate Kolmogorov-Smirnov test to define normal operational behaviour, whilst mitigating the requirement for a-priori knowledge. This reduces the computational complexity of the system whilst having the prospect to reduce the inherent noise within the high frequency bearing signal.Strong trends of degradation which can be used to derive prognostic maintenance conditions are observed, with sound statistical analysis performed. In particular, statistically significant degradation is found to occur 75 hours before failure occurred (representing identification at 54.2% of bearing life). Both the Kolmogorov-Smirnov statistic and P -value are employed as health metrics to which degradation can be inferred from. A series of 4 experiments is presented, showing the versatility of the described technique and cases where the technique cannot be employed.The technique is validated on a failed bearing and then verified on an independent, healthy bearing, and is shown to correctly identify the bearing of question in each case, enabling the prioritisation of maintenance actions which can be used to assist in reducing overall maintenance expenditure.

How to Cite

L. Godwin, J. ., & Matthews, P. . (2014). A Low Frequency Uni-variate Model for the Effective Diagnosis and Prognosis of Bearing Signals Based Upon High Frequency Data. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2350
Abstract 13 | PDF Downloads 14

##plugins.themes.bootstrap3.article.details##

Keywords

diagnosis, Bearing, Applied statistics, Kolmogorov-Smirnov, High frequency

References
Verma, A. and Kusiak A., (2011). Predictive Analysis of Wind Turbine Faults: A Data Mining Approach. in Proceedings of the 2011 Industrial Engineering Research Conference, Reno, Nevada, May 19-23,1 – 9.

Levrat, E., Iung, B., Crespo, (2008). E-maintenance:f review and conceptual framework. Production Planning & Control 19 (4), 408429.

Hameed, Z., Hong, Y., Cho, Y., Ahn S., and Song, C., (2009). Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable energy reviews, 13(1), 1–39.

Lee, J., Qiu, H., Yu, G., Lin, J., and Rexnord Technical Services (2007). 'Bearing Data Set', IMS, University of Cincinnati. NASA Ames Prognostics Data Repository, [http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data- repository/], NASA Ames, Moffett Field, CA.

Godwin, J. L., & Matthews, P. C., (2014a) Robust Statistical Methods for Rapid Data Labelling. In V. Bhatnagar, "Data Mining and Analysis in the Engineering Field". IGI Global : Hershey, PA, USA.

Baydar, N., Chen, Q., Ball, A. and Kruger, U. (2001).Detection of incipient tooth defect in helical gears using multivariate statistics. Mechanical Systems and Signal Processing, 15 (2), 303—321.

Rai, V. K., & Mohanty, A. R. (2007). Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert– Huang transform. Mechanical Systems and Signal Processing, 21(6), 2607-2615.

Zappalà, D., Tavner, P., Crabtree, C., & Sheng, S. (2013, January). Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis. In European Wind Energy Association (EWEA) Conference, Vienna, Austria.

Zappalà, D., Tavner, P. J., & Crabtree, C. J. (2012). Gear fault detection automation using WindCon frequency tracking. In Proceedings European Wind Energy Conference.

Ho, D., Randall, R.B., (2000) Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals, Mechanical Systems and Signal Processing 14 (5) pp. 763–788

N.T. van der Merwe, A.J. Hoffman, (2002). A modified cepstrum analysis applied to vibrational signals, in: Proceedings of 14th International Conference on Digital Signal Processing (DSP2002), vol. 2, Santorini, Greece, 2002, pp. 873–876

Raffiee, J., Raffiee, M., Tse, P., 2010. Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications 37 (6), 4568 - 4579.

Peng, Z., Chu, F., (2004). Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing 18 (2), 199 - 221.

Lin, Zuo, M., (2003). Gearbox fault diagnosis using adaptive wavelet filter. Mechanical Systems and Signal Processing 17 (6), 12591269.

Jardine, A. K., Lin, D., Banjevic, D., (2006). A review on machinery diagnostics and prognostics implementing
condition-based maintenance. Mechanical Systems and Signal Processing 20 (7), 1483 - 1510.

Bechhoefer, E., Qu, Y., Zhu, J. and He, D. (2013) Signal Processing Techniques to Improve an Acoustic Emissions Sensor. In proceedings of the annual conference of the PHMsociety October 14 – 17, New Orleans, LA, USA. 4(3). pp. 18.

Bechhoefer, E., & Kingsley, M. (2009). A review of time synchronous average algorithms. In Annual conference of the prognostics and health management society.

Heng, R.B.W., Nor. M.J.M., (1998) Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition. Applied Acoustics, 53 (1-3), pp. 211– 226

Tandon, N.. (1994) Comparison of some vibration parameters for the condition monitoring of rolling element bearings. Measurement: Journal of the International Measurement Confederation, 12 (3), pp. 285–289

Feng, Y., Qiu, Y., Crabtree, C., Long, H. & Tavner P., (2012) Monitoring wind turbine gearboxes. Wind Energy. Vol 16(5). pp. 728 – 740.

Crabtree, C.J. (2010). Survey of Commercially Available Condition Monitoring Systems for Wind Turbines, SuperGen Wind.

Stephens, M. A. (1974). EDF Statistics for Goodness of Fit and Some Comparisons. Journal of the American Statistical Association (American Statistical Association) 69 (347): 730–737

Cong, F., Chen, J., & Pan, Y. (2011). Kolmogorov-Smirnov test for rolling bearing performance degradation assessment and prognosis. Journal of Vibration and Control, 17(9), 1337-1347.

Anderson, T. W., & Darling, D. A. (1954). A test of goodness of fit. Journal of the American Statistical Association, 49(268), 765-769.

Bendre, S. M. (1989). Masking and swamping effects on tests for multiple outliers in normal sample. Communications in Statistics-Theory and Methods, 18(2), 697-710.

Leemis, L. M. (1995). Reliability: probabilistic models and statistical methods. Prentice-Hall, Inc.
Section
Technical Papers