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
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