Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree
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Date
2012-05
Authors
Seera, Manjeevan Singh
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Publisher
Universiti Sains Malaysia
Abstract
In this thesis, a novel approach to detecting and diagnosing comprehensive fault conditions of Induction Motors (IMs) using an Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) is proposed. The model, known as FMM-CART, exploits the advantages of both FMM and the CART for undertaking data classification and rule extraction problems. Modifications to FMM and the CART are introduced in order for the resulting model to work efficiently. In order to compare the FMM-CART performance, benchmark data sets from motor bearing faults and from the UCI machine learning repository are used for analysis, with the results discussed and compared with those from other methods. The results show that FMM-CART is able to obtain comparable, if not better, accuracy rates with respect to those reported in the literature. Then, an IM model is first simulated with various faults, which is then followed by a series of experiments on real IMs. A non-invasive condition monitoring technique, i.e., the Motor Current Signature Analysis (MCSA), is applied to establish a database comprising stator current signatures under different fault conditions. A number of harmonics values are extracted from the Power Spectral Density (PSD) of the motor current signatures, and used as discriminative input features for fault detection and diagnosis with FMM-CART. A comprehensive list of IM fault conditions, viz. broken rotor bars, supply unbalanced, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates, i.e., more than 98.53% with all potential faulty and fault-free conditions combined. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are elicited from FMM-CART for analysis and understanding of different IM fault conditions.
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Keywords
Fault detection and diagnosis of induction motors , using the fuzzy min-max neural network