NDLI: Fault diagnosis of pneumatic actuator using adaptive network-based fuzzy inference system models and a learning vector quantization neural network

Content Provider	IEEE Xplore Digital Library
Author	Shi, L. Sepehri, N.
Copyright Year	2004
Description	Author affiliation: Dept. of Mech. & Autom., Shanghai Univ., China (Shi, L.)
Abstract	Fault diagnosis in pneumatic actuators is a very difficult task due to the inherent high nonlinearity and uncertainty. Developing models of nonlinear systems with adaptive network-based fuzzy inference systems (ANFISs) has recently received attention. Models that are built upon ANFISs overcome the disadvantages of ordinary fuzzy modeling and can be very suitable for generalized modeling of nonlinear plants. We set up a group of ANFIS models which are relatively common in practice, corresponding to various situations of a pneumatic actuator, including normal, low and high supply pressure. Considering the advantage that a learning vector quantization (LVQ) neural network has a powerful ability to classification, we then utilize a LVQ neural network as a fault diagnosis scheme by abstracting the data of ANFIS models as the input vectors for nonlinear plants. The effectiveness is demonstrated via experiments on a pneumatic actuator.
Starting Page	1887
Ending Page	1892
File Size	425184
Page Count	6
File Format	PDF
ISBN	0780383591
ISSN	10987576
DOI	10.1109/IJCNN.2004.1380898
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-07-25
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Pneumatic actuators Adaptive systems Fuzzy neural networks Fuzzy systems Vector quantization Neural networks Power system modeling Uncertainty Nonlinear systems
Content Type	Text
Resource Type	Article

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