Anomaly detection via data mining techniques for aircraft engine operation monitoring

سال انتشار: 1396
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 512

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شناسه ملی سند علمی:

IIEC14_006

تاریخ نمایه سازی: 26 مرداد 1397

چکیده مقاله:

Today, system monitoring as a key function of condition based maintenance (CBM) is provided by the development of sensor applications. Data mining is a practical tool that can be used to extract latent information fiom data generated by sensors. This study examines data mining anomaly detection algorithms for identifying abnormal events in aircraft engine operations. The algorithms under investigation are density-based spatial clustering of application with noise (DBSCAN), expectation maximization (EM) and K-means as well-known clustering algorithms as well as one class support vector machine (OCSVM) as one class classification algorithm. No need for predefined criteria or domain knowledge and capability of determining unknown anomalies are the advantages of the proposed algorithms. The dataset used in this study was recorded from flight data obtained from 791 flights of a twin engine commercial aircraft (i.e. Fokker 100) at the cruise phase. The results show the effectiveness of the DBSCAN and OCSVM in identifying anomalous operations.

کلیدواژه ها:

Aircraft engine operation anomaly detection ، Data mining ، Cluster based anomaly detection ، One class classification

نویسندگان

Hassan Gharoun

M.Sc student, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Mahdi Hamid

PhD student‘, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Farid Ghaderi

Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Mohammad Mahdi Nasiri

Assistant Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran