Power plant owners require their plants’ high reliability, availability and also reduction of the cost in today’s power generation industry. In addition, the power generation industry is faced with a reduction of experienced operators and sophistication of power generation equipment. Remote monitoring service provided by original equipment manufacturers (OEMs) has become increasingly popular due to growing demand for both improvement of plant reliability and solution of experienced operator shortage. Through remote monitoring service, customers can benefit from swift and appropriate operational support based on OEM’s know-how. Before implementation of remote monitoring, the customer and OEM often required repeated interchanges of information about operation and instrumentation data. These interchanges took a lot of time. Data analysis and estimation of deterioration were time-consuming. Remote monitoring has enabled us, OEMs, not only to access to a plant’s real-time information but also to trace the historical operation data, and therefore the required time of data analysis and improvement has been reduced. Mitsubishi Heavy Industries, Ltd. also embarked on around-the-clock remote monitoring service for gas turbine plant over a decade ago and has increased its ability over time. At present, the application of remote monitoring systems have been extended not only into proactive maintenance by making use of diagnostic techniques carried out by expert engineers but also into building a pattern recognition system and an artificial intelligence system using expert’ knowledge. Conventional diagnostics is only determining whether the plant is being operated within the prescribed threshold levels. Pattern recognition is a state-of-the-art technique for diagnosing plant operating conditions. By comparing past and present conditions, small deterioration can be detected before it needs inspection or repair, while all the operating parameter is within their threshold levels. Mahalanobis-Taguchi method (MT method) is a technique for pattern recognition and has the advantage of diagnosing overall GT condition by combining many variables into one indicator called Mahalanobis distance. MHI has applied MT method to the monitoring of gas turbines and verified it to be efficient method of diagnostics. Now, in addition to the MT method, automatic abnormal data discrimination system has been developed based on an artificial intelligence technique. Among a lot of artificial intelligence techniques, Bayesian network mathematical model is used.
- International Gas Turbine Institute
Advanced Gas Turbine Diagnostics Using Pattern Recognition
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Kumano, S, Mikami, N, & Aoyama, K. "Advanced Gas Turbine Diagnostics Using Pattern Recognition." Proceedings of the ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. Volume 3: Controls, Diagnostics and Instrumentation; Education; Electric Power; Microturbines and Small Turbomachinery; Solar Brayton and Rankine Cycle. Vancouver, British Columbia, Canada. June 6–10, 2011. pp. 179-187. ASME. https://doi.org/10.1115/GT2011-45670
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