DocumentCode :
2287751
Title :
Detection of Bearing Failure in Rotating Machine Using Adaptive Neuro-Fuzzy Inference System
Author :
Wadhwani, Sulochana ; Wadhwani, A.K. ; Gupta, S.P. ; Kumar, Vinod
Author_Institution :
Madhav Inst. of Technol. & Sci., Gwalior
fYear :
2006
fDate :
12-15 Dec. 2006
Firstpage :
1
Lastpage :
5
Abstract :
This paper proposes a novel approach for bearing health evaluation using Lempel-Ziv complexity and time domain statistical parameters in conjunction with ANFIS. Compared to conventional techniques the presented approach works well for a non linear physical system and is thus suited for condition monitoring of machine system under varying operating and loading conditions. The performance of this technique is investigated through experimental study of realistic vibration signals. The results demonstrate that complexity analysis and time domain parameters in conjunction with ANFIS provide an effective measure forebearing health evaluation.
Keywords :
adaptive systems; condition monitoring; electric machine analysis computing; failure analysis; fault diagnosis; fuzzy neural nets; inference mechanisms; machine bearings; statistical analysis; vibrations; ANFIS; Lempel-Ziv complexity; adaptive neuro-fuzzy inference system; bearing failure detection; condition monitoring; health evaluation; non linear physical system; rotating machine; time domain statistical parameters; vibration signals; Adaptive systems; Condition monitoring; Fault detection; Fault diagnosis; Frequency; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Neural networks; Rotating machines; ANFIS; Bearing fault; Fault detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Electronics, Drives and Energy Systems, 2006. PEDES '06. International Conference on
Conference_Location :
New Delhi
Print_ISBN :
0-7803-9772-X
Electronic_ISBN :
0-7803-9772-X
Type :
conf
DOI :
10.1109/PEDES.2006.344317
Filename :
4148024
Link To Document :
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