Fuzzy inference systems implemented on neural architectures formotor fault detection and diagnosis
Altug, S.
Mo-Yuen Chen
Trussell, H.J.
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Dec 1999
Volume: 46,
Issue: 6
On page(s): 1069-1079
ISSN: 0278-0046
References Cited: 46
CODEN: ITIED6
INSPEC Accession Number: 6443306
Digital Object Identifier: 10.1109/41.807988
Current Version Published: 2002-08-06
Abstract
Motor fault detection and diagnosis involves processing a large
amount of information of the motor system. With the combined synergy of
fuzzy logic and neural networks, a better understanding of the
heuristics underlying the motor fault detection/diagnosis process and
successful fault detection/diagnosis schemes can be achieved. This paper
presents two neural fuzzy (NN/FZ) inference systems, namely, fuzzy
adaptive learning control/decision network (FALCON) and adaptive network
based fuzzy inference system (ANFIS), with applications to induction
motor fault detection/diagnosis problems. The general specifications of
the NN/FZ systems are discussed. In addition, the fault
detection/diagnosis structures are analyzed and compared with regard to
their learning algorithms, initial knowledge requirements, extracted
knowledge types, domain partitioning, rule structuring and
modifications. Simulated experimental results are presented in terms of
motor fault detection accuracy and knowledge extraction feasibility.
Results suggest new and promising research areas for using NN/FZ
inference systems for incipient fault detection and diagnosis in
induction motors
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