fastICA 기반 발전기 고장 진단 및 예측

Abstract

Purpose: Failure in thermal power plant generators has high safety and financial risk. Diagnostics and Prognostics to detect abnormality in advance are crucial for failure prevention. Methods: In this research, fast independent component analysis was applied to select key features from sensor data, and abnormalities were detected when unnatural variation existed in multivariate control charts. Results: The proposed framework was applied to the dataset acquired from a thermal power plant, and exhibited promising results in detecting and predicting incipient failures. Conclusion: From the analytical results of an example, it was found that the proposed methodology has potential in failure diagnostics and prognostics to increase the availability of facilities through early detection of incipient failures.