Published in: 14th International Industrial Engineering Conference
COI code: IIEC14_006
Paper Language: English
How to Download This Paper
For Downloading the Fulltext of CIVILICA papers please visit the orginal Persian Section of website.
Authors Anomaly detection via data mining techniques for aircraft engine operation monitoringHassan 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
COI code: IIEC14_006
how to cite to this paper:If you want to refer to this article in your research, you can easily use the following in the resources and references section:
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)
For a complete overview of how to citation please review the following CIVILICA Guide (Citation)
The University/Research Center Information:
Type: state university
Paper No.: 55403
in University Ranking and Scientometrics the Iranian universities and research centers are evaluated based on scientific papers.
Research Info Management
Export Citation info of this paper to research management softwares
New Related Papers
- Determining of the Capacity of the Jacket Platforms by Non-Linear Pushover Analysis
- Overview of Persian Gulf Bridge Caisson Lowering Procedure and Proposing a New Method
- The Korean Strategy of Enhancing Offshore Equipment’s Industry by Entering Offshore Service Market
- Rehabilitation of jacket offshore platforms
- Degree of Bending in Tubular K-joints of Jacket Structures under OPB Loading
The Above articles are recently indexed in the related subjects
Iran Scientific Advertisment Netword
Share this paper
WHAT IS COI?
COI is a national code dedicated to all Iranian Conference and Journal Papers. the COI of each paper can be verified online.