A new auto-encoder-based dynamic threshold to reduce false alarm rate for anomaly detection of steam turbines

•The proposed method can model the normal data with denoising and ensemble technique.•Dynamic threshold is newly developed to minimize false alarms in anomaly detection.•Sensitivity is newly defined to identify condition parameters related to an anomaly.•New metrics are defined to validate the anoma...

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Veröffentlicht in:	Expert systems with applications 2022-03, Vol.189, p.116094, Article 116094
Hauptverfasser:	Ko, Jin Uk, Na, Kyumin, Oh, Joon-Seok, Kim, Jaedong, Youn, Byeng D.
Format:	Artikel
Sprache:	eng
Schlagworte:	Anomalies Anomaly detection Coders Deep learning Dynamic threshold Ensemble denoising auto-encoder False alarms Noise reduction Performance measurement Sensitivity Steam turbine Steam turbines Thermal power plants
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