Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II
Joint Authors
Liu, Peng-yuan
Li, Bing
Han, Cui-e
Wang, Feng
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-21
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
A novel feature extraction and selection scheme is presented for intelligent engine fault diagnosis by utilizing two-dimensional nonnegative matrix factorization (2DNMF), mutual information, and nondominated sorting genetic algorithms II (NSGA-II).
Experiments are conducted on an engine test rig, in which eight different engine operating conditions including one normal condition and seven fault conditions are simulated, to evaluate the presented feature extraction and selection scheme.
In the phase of feature extraction, the S transform technique is firstly utilized to convert the engine vibration signals to time-frequency domain, which can provide richer information on engine operating conditions.
Then a novel feature extraction technique, named two-dimensional nonnegative matrix factorization, is employed for characterizing the time-frequency representations.
In the feature selection phase, a hybrid filter and wrapper scheme based on mutual information and NSGA-II is utilized to acquire a compact feature subset for engine fault diagnosis.
Experimental results by adopted three different classifiers have demonstrated that the proposed feature extraction and selection scheme can achieve a very satisfying classification performance with fewer features for engine fault diagnosis.
American Psychological Association (APA)
Liu, Peng-yuan& Li, Bing& Han, Cui-e& Wang, Feng. 2016. Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II. Shock and Vibration،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1119080
Modern Language Association (MLA)
Liu, Peng-yuan…[et al.]. Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II. Shock and Vibration No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1119080
American Medical Association (AMA)
Liu, Peng-yuan& Li, Bing& Han, Cui-e& Wang, Feng. Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1119080
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1119080