Predictive Condition Monitoring of Induction Motor Bearing Using Fuzzy Logic

Research Area: Volume 1 Issue 5, Sept. 2012 Year: 2012
Type of Publication: Article Keywords: Induction Motor IM, Artificial Intelligence AI, Fuzzy Logic, Fuzzy logic system FLS
  • Prof. Rakeshkumar A. Patel
Journal: IJEIR Volume: 3
Number: 1 Pages: 451-454
Month: Sept.
ISSN: 2277-5668
Induction motor is critical component in industrial processes and is frequently integrated in commercially available equipment. Safety, reliability, efficiency and performance are the major concerns of induction motor applications. Due to high reliability requirements and cost of breakdown, condition monitoring, diagnosis and Protection increasing importance. Protection of an induction motor (IM) against possible problems, such as stator faults, rotor faults and mechanical faults, occurring in the course of its operation is very important, because it is very popular in industries. Bearing fault is well known mechanical fault of IM.41% faults related to bearing in IM. To avoid break down of IM condition monitoring of motor bearing condition is very important during the normal operation. Various classical and AI techniques like fuzzy logic, neural network, neuro-fuzzy are used for condition monitoring and diagnosis of IM. Among the above mentioned AI techniques, Fuzzy logic is the best technique for condition monitoring and diagnosis of IM bearing condition. Therefore, the present paper focuses on fuzzy logic technique. In this paper Fuzzy logic is design for the condition monitoring and diagnosis of induction motor bearing condition using motor current and speed. After applying Fuzzy logic it has been seen that continuous monitoring of the current and speed values of the motor conditioned monitoring and diagnosis of induction motor bearing condition can be done.

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