Abstract
The purpose of this paper is to construct a smart induction motor fault diagnosis system with cerebellar model articulation controller (CMAC). First, we divide induction motor faults in three kinds, rotor mandrel fault, bearing fault and electrical fault. Then, we subdivide them into ten types, in each of which the vibration signal spectra of induction motors were measured and sorted for establishing the individual fault types. From the information on motor faults, we identify representative characteristic frequency spectra for the faults to further establish the correlations between each of the fault types and its corresponding characteristic frequency spectrum as the basis for the development of a motor fault diagnosis system. In this paper, the theoretic basis is CMAC, and the data of vibration signal spectrum measured against the motor faults are used to train the fault diagnosis system. We then conducted fault diagnoses with the data of actual motor run. The test results demonstrated that the proposed induction motor fault diagnosis system is capable of fast algorithm, requires less data to train with, as well as has excellent power of identification.
Highlights
Motors play an arduous role in industries
Because cerebellar model articulation controller (CMAC) has a training process, and the training data is the characteristic frequency spectrum of vibration signals extracted from the different fault categories of the motor, the proposed fault diagnosis method can be applied to motors with different capacities
This paper constructs a motor fault diagnosis model with the algorithm of CMAC, which is capable of nonlinear mapping
Summary
Motors play an arduous role in industries. Due to their high economic benefit and robustness, they are extensively employed in all sorts of production equipment. There are other algorithms used in the study of motor fault diagnoses; examples are the neuralfuzzy [1] and the approximate reasoning [3] The former is used in a method that does not require a mathematical model built for motors, but builds a fault diagnosis system based on the correlations between inputs and outputs; yet, the algorithm it uses takes longer time to arrive at optimal solutions and makes only one single record in data recognition. An intelligent fault diagnosis of three-phase induction motors using a signal-based method was proposed in [17] This technique was tested in different situations to demonstrate its effectiveness in fault diagnoses, even when the information about operating modes is limited or difficult to obtain. This technique allows workers on site to locate the positions of fault in less time, speeding up the service, and allows the specialists on site to determine the length of period of system downtime based on the messages obtained from the fault diagnosis, increasing overall economic benefit
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