Development of ANN for diagnosing induction motor bearing failure

Mahamad, Abd Kadir and Hiyama, Takashi (2010) Development of ANN for diagnosing induction motor bearing failure. IEEJ Transactions on Industry Applications, 130 .

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Abstract

Today, various diagnosis methods exist to diagnose bearing failure in industries such as artificial neural network (ANN), fuzzy logic, genetic algorithm and statistical analysis. This paper proposes the development of ANN model of induction motor bearing (IMB) fault diagnosis using different network types and structures. In this case Feedforward Neural Network (FFNN) and Elman Network types with Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) structures were studied. The raw data used in this work was obtained from the Case Western Reserve University website in form of vibration signal. During pre-processing stage, Fast Fourier Transform (FFT) and enveloping techniques were applied to raw data before it was fed to features extraction stage. A total of 16 features were extracted from both time and frequency domain respectively. Subsequently, a distance evaluation technique was used for features selection, where 9 salient features were selected for ANN fault diagnosis. In the development of ANN fault diagnosis, FFNN and Elman Network were utilized with training algorithm of Levenberg Marquart Backpropagation. The result indicates the performance of classified IMB fault by using MIMO Elman structure which was better compared to other combination structures.

Item Type:Article
Uncontrolled Keywords:IMB; ANN; MIMO; MISO; distance evaluation technique
Subjects:T Technology > TJ Mechanical engineering and machinery > TJ1040-1119 Machinery exclusive of prime movers
Divisions:Faculty of Electrical and Electronic Engineering > Department of Computer Engineering
ID Code:3025
Deposited By:Normajihan Abd. Rahman
Deposited On:13 Feb 2013 16:46
Last Modified:22 Jan 2015 11:48

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