Vibration for detection and diagnosis bearing faults using adaptive neurofuzzy inference system
Author
Joint Authors
Djamila, Bouneb
Hisham, Murabit
Source
Issue
Vol. 14, Issue 1 (31 Mar. 2018), pp.95-104, 10 p.
Publisher
Publication Date
2018-03-31
Country of Publication
Algeria
No. of Pages
10
Main Subjects
Natural & Life Sciences (Multidisciplinary)
Abstract EN
The fault diagnosis of electrical machines is a primordial and necessary task in industry.
The failure is unbearable because it causes, incontestably, decrease in production and increases cost repair.
Induction motors are the most important equipment in industry, where reliability and safe operation is desirable, for maintenance, such as detection, and diagnosis of mechanical and electrical defects of electric drives.
The several techniques are adopted and frequency analysis is the most widely used.
Artificial intelligence techniques was gained popularity last decay’s in numerous applications.
The presented results show the detected and diagnosed, of the bearing faults of the induction motor, based on Adaptive Neuro-Fuzzy Inference System.
The vibrations analysis of the induction machine using the Artificial Intelligence Techniques, combining neural networks and fuzzy logic has been applied successfully.
The designed ANFIS network shows about 99% accurate results as validated by Mat lab / Simulink simulation.
American Psychological Association (APA)
Djamila, Bouneb& Tahar, Bahi& Hisham, Murabit. 2018. Vibration for detection and diagnosis bearing faults using adaptive neurofuzzy inference system. Journal of Electrical Systems،Vol. 14, no. 1, pp.95-104.
https://search.emarefa.net/detail/BIM-836002
Modern Language Association (MLA)
Djamila, Bouneb…[et al.]. Vibration for detection and diagnosis bearing faults using adaptive neurofuzzy inference system. Journal of Electrical Systems Vol. 14, no. 1 (2018), pp.95-104.
https://search.emarefa.net/detail/BIM-836002
American Medical Association (AMA)
Djamila, Bouneb& Tahar, Bahi& Hisham, Murabit. Vibration for detection and diagnosis bearing faults using adaptive neurofuzzy inference system. Journal of Electrical Systems. 2018. Vol. 14, no. 1, pp.95-104.
https://search.emarefa.net/detail/BIM-836002
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 103-104
Record ID
BIM-836002