NDLI: Machine degradation analysis using fuzzy CMAC neural network approach

Content Provider	Springer Nature Link
Author	Xu, Rong Zhen Xie, Le Zhang, Min Cong Li, Cong Xin
Copyright Year	2007
Abstract	Traditionally, machine condition is described as a dichotomous problem: normal or failure. In fact, machines go through a series of degrading states until failure occurs. Degrading states do not make machines breakdown, but they do decrease performance reliability and increase the potential for faults and failures. In this paper, a neural network approach based on fuzzy cerebellar model articulation controller (FCMAC) is proposed to analyze machine degradation severity. Two kinds of situations are discussed: (1) machine signature from different levels of degradation severity are available to train the network; (2) machine signature from only normal perfect state are available to train the network. A degradation index is developed to reflect the degradation degree. The FCMAC network works as a ‘classifier’ or a ‘condition discriminator’ in these two situations. An example is presented to demonstrate this method and the results show that the FCMAC network is capable of ranking machine degradations quantitatively in both situations. With degradation analysis, maintenance activities can be implemented before failure eventually occurs, thus preventing fatal breakdowns more effectively.
Starting Page	765
Ending Page	772
Page Count	8
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	36
Issue Number	7-8
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-01-13
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Degradation analysis Degradation index Fuzzy logic Neural network Cerebellar model articulation controller (CMAC) Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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