NDLI: Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS

Content Provider	IET Digital Library
Author	Helmi, Hamed Forouzantabar, Ahmad
Abstract	Demands for various products, higher qualities, reduction of costs and competitiveness, have resulted in the use of intelligent fault detection systems. Bearing fault diagnosis as a major component of the electric motors has had an essential role in the operation of production units’ reliability. In addition, vibration analysis is one of the most powerful tools in diagnostics. Advances in signal processing technology and electrical equipment have developed a machinery condition monitoring for defect detection. This study has used the extracted features of vibration signals and the adaptive neuro-fuzzy interface system (ANFIS) network proposing a structure for fault detection and diagnosis of rolling bearings. Time-domain and frequency-domain statistical characteristics have been extracted fault information from vibration signals. Besides, the test data sets are presented to the ANFIS network. Simulation results indicated that the performance of the ANFIS network is acceptable. The results reveal that this method has more accuracy and better classification performance in comparison with other methods proposed in the literature.
Starting Page	662
Ending Page	669
Page Count	8
ISSN	17518660
Volume Number	13
e-ISSN	17518679
Issue Number	Issue 5, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-epa/13/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-epa.2018.5274
Journal	IET Electric Power Applications
Publisher Date	2018-07-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Neuro-fuzzy Interface System ANFIS Network Civil And Mechanical Engineering Computing Classification Performance Condition Monitoring Defect Detection Electric Motor Electrical Equipment Fault Diagnosis Fault Information Feature Extraction Frequency Domain Analysis Frequency Domain Feature Extraction Frequency-domain Statistical Characteristic Fuzzy Neural Nets Fuzzy Reasoning Inspection And Quality Control Intelligent Fault Detection System Knowledge Engineering Technique Machinery Condition Monitoring Maintenance And Reliability Mathematical Analysis Mechanical Component Mechanical Engineering Mechanical Engineering Application of IT Neural Computing Technique Production Units Rolling Bearing Fault Detection Rolling Bearings Signal Processing And Detection Statistics Time Domain Feature Vibration Analysis Vibration Signal Vibrational Signal Processing Vibrations Vibrations And Shock Wave
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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