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Anomaly detection via data mining techniques for aircraft engine operation monitoring

Credit to Download: 1 | Page Numbers 16 | Abstract Views: 132
Year: 2017
COI code: IIEC14_006
Paper Language: English

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Authors Anomaly detection via data mining techniques for aircraft engine operation monitoring

  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

Abstract:

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.

Keywords:

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

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COI code: IIEC14_006

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Gharoun, Hassan; Mahdi Hamid; Farid Ghaderi & Mohammad Mahdi Nasiri, 2017, Anomaly detection via data mining techniques for aircraft engine operation monitoring, 14th International Industrial Engineering Conference, تهران, انجمن مهندسي صنايع ايران - دانشگاه علم و صنعت ايران, https://www.civilica.com/Paper-IIEC14-IIEC14_006.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Gharoun, Hassan; Mahdi Hamid; Farid Ghaderi & Mohammad Mahdi Nasiri, 2017)
Second and more: (Gharoun; Hamid; Ghaderi & Nasiri, 2017)
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Scientometrics

The University/Research Center Information:
Type: state university
Paper No.: 55403
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