NDLI: Monitoring of rotary machine by mean of self adaptive growing neural network

Content Provider	IEEE Xplore Digital Library
Author	Barakat, M. Druaux, F. Lefebvre, D. Khalil, M. Mustapha, O.
Copyright Year	2011
Description	Author affiliation: Lebanese University AZEM center (Lebanon) (Lefebvre, D.) \|\| Islamic university of Lebanon, Lebanon (Khalil, M.) \|\| University of Le Havre (France) (Barakat, M.; Druaux, F.; Mustapha, O.)
Abstract	For the improvement of reliability, safety and efficiency advanced methods of supervision, fault detection and fault diagnosis become increasingly important for many technical processes. To determine the condition of an inaccessible fault in an operating mechanical system, the vibration signal of the machine is continuously monitored by placing sensors close to the source of the vibrations. These signals are further processed to extract the features and identify the status of the machine. This paper deals with a new scheme for the detection and diagnosis of localized defects in machine rotating components based on Self Adaptive Growing Neural Network (SAGNN) classifier which is a novel approach to pattern classification propounded for tackling fault diagnosis tasks. Feature vector is extracted from signal in time domain and SAGNN is trained and used as a diagnosis classifier. In order to evaluate SAGNN, the data sets obtained from vibration signals are classified with both SAGNN and Radial Basis Function (RBF) classifiers. The proposed algorithm is applied to the fault diagnosis of mechanical rotary machine, and the test results show that classification by mean of SAGNN identify the faults of rotary elements (Gears, bearing and belts) more accurately and has better diagnosis performance compared to the RBF.
Starting Page	132
Ending Page	137
File Size	654867
Page Count	6
File Format	PDF
ISBN	9781457701245
e-ISBN	9781457701252
DOI	10.1109/MED.2011.5983017
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Fault diagnosis Gears Neurons Feature extraction Biological neural networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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