Combining expert knowledge and unsupervised learning techniques for anomaly detection in aircraft flight data
| Autor: | Hamed Khorasgani, Daniel L. C. Mack, Gautam Biswas, Raj Bharadwaj, Dinkar Mylaraswamy |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science 02 engineering and technology Machine learning computer.software_genre Computer Science Applications 020901 industrial engineering & automation Control and Systems Engineering 020204 information systems 0202 electrical engineering electronic engineering information engineering Unsupervised learning Anomaly detection Artificial intelligence Electrical and Electronic Engineering business Flight data computer |
| Zdroj: | at - Automatisierungstechnik. 66:291-307 |
| ISSN: | 2196-677X 0178-2312 |
| DOI: | 10.1515/auto-2017-0120 |
| Popis: | Fault detection and isolation schemes are designed to detect the onset of adverse events during operations of complex systems, such as aircraft, power plants, and industrial processes. In this paper, we combine unsupervised learning techniques with expert knowledge to develop an anomaly detection method to find previously undetected faults from a large database of flight operations data. The unsupervised learning technique combined with a feature extraction scheme applied to the clusters labeled as anomalous facilitates expert analysis in characterizing relevant anomalies and faults in flight operations. We present a case study using a large flight operations data set, and discuss results to demonstrate the effectiveness of our approach. Our method is general, and equally applicable to manufacturing processes and other industrial applications. |
| Databáze: | OpenAIRE |
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