A Multi-Working States Sensor Anomaly Detection Method Using Deep Learning Algorithms

The data collected from sensors are subject to the presence of anomaly data. These anomalies may stem from sensor malfunctions or poor communication. Prior to the processing of the data, it is imperative to detect and isolate the anomaly data from the substantial volume of normal data. The utilizati...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 25; číslo 18; s. 5686
Hlavní autori:	Wu, Di, Koskinen, Kari, Coatanea, Eric
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 12.09.2025
Predmet:	Accuracy Behavior data-driven deep learning Influence LSTM Methods Neural networks Physics sensor anomaly detection Sensors Support vector machines Working conditions sensor anomaly detection deep learning LSTM data-driven
ISSN:	1424-8220, 1424-8220
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