As a consequence of the recent deregulation in the electrical power production industry, new private entrepreneurs with no prior experience in the power plant operation have entered into the power generation business. They hedge the business risks by outsourcing the operation and maintenance activities to third party service providers with whom they share risks/rewards of plant performance. The main maintenance providers are the original equipment manufacturers, who are responsible for the majority of the cost associated with unplanned outages. With the cost-benefit of preventing such unplanned outage as a gas turbine compressor failure hovering around the twenty million dollars mark, techniques for detecting failure precursors to avoid or limit the number of systems catastrophic failure are necessary. In this paper, a methodical process is proposed to detect precursory events that lead to catastrophic systems failure. The wavelet packet transform is used to perform multi-resolution analysis of gas turbines health, condition and vibration sensors data to extract their signal features. Then the probabilistic principal component analysis is utilized to fuse them into a few uncorrelated variables. Next a one-dimensional signal representing the multi-variables data is computed. After that the statistical process control techniques is applied to set the anomaly threshold. Finally, a Bayesian hypothesis testing method is applied for abnormality detection to the monitored signal. As a proof of concept, the proposed process is successfully applied to a gas turbine compressor failure precursor detection.
How to Cite
Chui, C. (1992). An introduction to wavelets, Academic Pr.
Daubechies, I. (1992). Ten lectures on wavelets, Society for Industrial Mathematics.
Diallo, O. and D. Mavris (2010). A Concept for Intelligent Fault Detection Using a Multi-resolution Analysis. Aerospace Systems Conference, Los Angeles, CA.
EPRI (2007). Gas turbine compressor dependability: investigation of the MS7001F /9001F R0 compressor stage blade designs and failures, Electric Power Research Institute (EPRI).
EPRI (2008). GE FA Compressor Dependability (phase 2), Electric Power Research Institute (EPRI).
Graps, A. (1995). "An introduction to wavelets." IEEE Computational Science & Engineering 2(2): 50-61.
Jiang, X. and H. Adeli (2004). "Wavelet packet- autocorrelation function method for traffic flow pattern analysis." Computer-aided civil and infrastructure engineering 19(5): 324-337.
Jiang, X., S. Mahadevan, et al. (2006). "Bayesian wavelet packet denoising for structural system identification." Progress in Structural Engineering and Materials 14(2): 333-356.
Matlab (1999-2009). Wavelet Toolbox 4 -User's Guide. Matlab 7.8.0 (R2009a).
Misrikhanov, A. (2006). "Wavelet transform methods: Application in electroenergetics." Automation and Remote Control 67(5): 682-697.
Montgomery, D. (1985). "Introduction to statistical quality control."
Montgomery, D. (1996). Introduction to statistical quality control, Wiley- New York.
Stoll, H. (2001). "Creating owner’s competitive advantage through contractual services." GER- 4208, GE Power Systems.
Sun, Z.(2002). "Structural damage assessment based on wavelet packet transform." Journal of structural engineering 128: 1354.
Sun, Z. and C. Chang (2004). "Statistical wavelet-based method for structural health monitoring." Journal of structural engineering 130: 1055.
Thaler, H. (2006). Global Gas Turbine Markets and Trends - What does the future hold? The Future of Gas Turbine Technology - 3rd International Conference, Brussels.
Tipping, M. and C. Bishop (1999). "Probabilistic principal component analysis." Journal of the Royal Statistical Society. Series B, Statistical Methodology: 611-622.
Wu, Y. and R. Du (1996). "Feature extraction and assessment using wavelet packets for monitoring of machining processes." Mechanical systems and signal processing 10(1): 29-53.
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