Many different techniques have been developed for detecting faults in rotating machinery. This is because different fault types typically require different techniques for the effective detection of the fault. However, for many new or unknown fault types, we have found that the existing detection techniques are either incapable or ineffective and that we therefore need to come up with brand new methods after the fault event. This can significantly constrain the usefulness and effectiveness of Prognostic Health Management (PHM) systems. In this paper we attempt to look at detecting global changes in the synchronously averaged signals as the machine’s health status progresses from healthy to faulty, and to define one unified signal processing technique and its associated condition indicators for the detection of changes caused by various types of faults in rotating machinery. The proposed method is conceptually very simple, and its effectiveness is demonstrated using vibration data from machines with several different types of faults. The results have shown that this single unified change detection approach can be very effective in detecting and trending changes caused by many different types of machine faults.
How to Cite
machine condition monitoring, generalized fault detection, unified change detection
Blunt, D.M and Keller, J.A. (2006). Detection of a fatigue crack in a UH-60A planet gear carrier using vibration analysis. Journal of Mechanical Systems and Signal Processing 20(8), pages 2095-2111.
Bonnardot, F., Badaoui, M.El, Randall, R.B., Daniere, J., and Guillet, F. (2005). Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation). Journal of Mechanical Systems and Signal Processing 19(6), pages 766-785.
Braun, S. (1975). Extraction of Periodic Waveforms by Time Domain Averaging. Acoustica 32, pages 69-77, 1975.
Burchill, R.F., Frarey, J.L. and Wilson, D.S. (1973). New machinery health diagnostic techniques using high- frequency vibration. SAE Paper 730930, 1973.
Forrester, B.D. (1996). Advanced Vibration Analysis Techniques for Fault Detection and Diagnosis in Geared Transmission Systems. Ph.D.Thesis, Swinburne University of Technology, Australia.
Galati, F.A., Forrester, B.D. and Dey, S. (2008). Application of the generalised likelihood ratio algorithm to the detection of a bearing fault in a helicopter transmission. Australian Journal of Mechanical Engineering 5(2),pages 169-176, 2008.
Galati, F.A. (2007). Investigation into Input Drive-Shaft Assembly Failures in RAN Sea King’s. Vertiflite Conference (AIAC12), 13-17 March 2007, Melbourne, Australia.
Hancock, K.M. and Zhang, Q. (2006). A Hybrid Approach to Hydraulic Vane Pump Condition Monitoring and Fault Detection. Transaction of the American Society of Agricultural and Biological Engineers (ASABE) 49(4): 1203−1211, 2006.
Larder, B.D. (1999). Helicopter HUM/FDR: Benefits and Developments. Proceedings of the 55th Annual Forum of the American Helicopter Society (AHS), 25-27 May 1999, Montreal, Canada.
Jarvis, M.P. and Sleight, P. (2011). Report on the Accident to Aerospatiale (Eurocopter) AS332 L2 Super Puma Registration G-REDL. Aircraft Accident Report 2/ 2011, Air Accidents Investigation Branch, Department of Transport, UK.
Lee, E. (2010). A simple HUMS approach to detect characteristic variation for mechanical systems. Prognostics and Health Management Conference (PHM '10, p1-8), 12-14 Jan 2010, Macau, China.
Lei, Y.G., Lin, J., He, Z.J., and Zuo, M.J. (2013). A review on empirical mode decomposition in fault diagnosis of rotating machinery. Journal of Mechanical Systems and Signal Processing 35, pp. 108-126, 2013.
Man, Z.H., Wang, W., Khoo, S.Y., and Yin, J.L. (2012). Optimal Sinusoidal Modelling of Gear Mesh Vibration Signals for Gear Diagnosis and Prognosis. Journal of Mechanical Systems and Signal Processing 33(11), pp. 256-274, Nov. 2012.
Mcfadden, P.D. and Toozhy, M.M. (2000). Application of Synchronous Averaging to Vibration Monitoring of Rolling Element Bearings. Journal of Mechanical Systems and Signal Processing 14(6), pp. 891-906, June 2000.
Randall, R.B. and Antoni, J. (2011). Rolling element bearing diagnostics – A Tutorial. Journal of Mechanical Systems and Signal Processing 25, pp. 485- 520, 2011.
Stewart, R.M. (1977). Some useful data analysis techniques for gearbox diagnostics. Technical report paper MHM/R/10/77, University of Southampton, Institute of Sound and Vibration Research, 1977.
Vavlitis, C. (1998). Crack Growth Behaviour of Spur Gears: A Fractographic Analysis. DSTO-TN-0137, Defence Science and Technology Organisation, Jan 1998, Australia.
Vecer, P., Kreidl, M. and Smid, R. (2005). Condition Indicators for Gearbox Condition Monitoring Systems. Acta Polytechnica Report Vol. 45, No. 6/2005, Czech Technical University Publishing House, Prague, Czech Republic.
Wang, W.Y. and Harrap, M.J. (1996). Condition Monitoring of Ball Bearings Using Envelope Autocorrelation Technique. The International Journal of Machine Vibration (5), pages 34-44, 1996.
Wang, W. and Wong, A.K. (2000). Linear Prediction and Gear Fault Diagnosis. Proceedings of the 13th International Conference of Condition Monitoring and Diagnostic Engineering Management (COMADEM) 2000, pages 305-315, December 3-8, 2000, Houston, Texas, USA.
Wang, W. (2001). Early Detection of Gear Tooth Cracking Using the Resonance Demodulation Technique. Mechanical System and Signal processing 15(5), pages 887-903.
Wang, W. and Keller, J.A. (2007). A Novel Technique of Crack Detection for HelicopterthMain Gearbox Planet Carrier. Proceedings of the 5 DSTO International Conference on Health and Usage Monitoring (HUMS 2007), 13-17 March 2007, Melbourne, Australia.
Wang, W. (2008). Autoregressive Model-Based Diagnostics for Gears and Bearings. Insight – Non-Destructive Testing and Condition Monitoring 50(8), pages 414- 418, August 2008.
Wang, W. and Muschlitz, G. (2010). Disk Crack Detection in Spin Testing Using Tip Timing Data. 31st IEEE Aerospace Conference, March 6-13 2010, Big Sky, Montana, USA.
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