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Proceedings Papers

Detection of Machinery Failure Signs From Big Time-Series Data Obtained by Flow Simulation of Intermediate-Pressure Steam Turbines

Kazuhiko Komatsu, Hironori Miyazawa, Cheng Yiran, Masayuki Sato, Takashi Furusawa, Satoru Yamamoto, Hiroaki Kobayashi
Proc. ASME. GT2021, Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power, V004T09A002, June 7–11, 2021
Paper No: GT2021-58698
DOI: https://doi.org/10.1115/GT2021-58698
... Sendai, 980-8579 Sendai, 980-8579 Japan Japan Japan ABSTRACT NOMENCLATURE Depth of erosion The periodic maintenance, repair, and overhaul (MRO) of d Number of clusters Number of feature points turbine blades in thermal power plants are essential to maintain K a stable power supply. During MRO, older...
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Topics: Failure, Flow simulation, Machinery, Pressure, Steam turbines, Time series
Proceedings Papers

Split Training Method to Generate Data Driven Model for Gas Turbine Engine Anomaly Detection

Yu Takiguchi, Satoshi Shiono
Proc. ASME. GT2020, Volume 5: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage, V005T05A027, September 21–25, 2020
Paper No: GT2020-16062
DOI: https://doi.org/10.1115/GT2020-16062
... outputs and control inputs from pilots. More accurate trend monitoring of the relationship among engine parameters enables earlier anomaly detection with high accuracy. Machine learning techniques such as clustering, nonlinear regression, classification and optimization are used in STM. The input data...
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Topics: Gas turbines, Machine learning, Optimization, Engines, Simulation models, Shapes, Algorithms, Flight, Sensors, Simulation