Abstract
This paper proposes a feature selection (FS) approach, namely, correlation and fitness value-based feature selection (CFFS). CFFS is an improvement feature selection approach of correlation-based feature selection (CFS) for the common failure cases of the induction motor. CFFS establishes the induction motor fault detection (FD) system with artificial neural network (ANN). This study analyzes the current signal of the induction motor with multiresolution analysis (MRA), extracts the features, and uses feature selection approaches (ReliefF, CFS, and CFFS) to reduce the number of features and maintain the accuracy of the induction motor fault detection system. Finally, the induction motor fault detection system is trained by the feature selection approaches selected features. The best induction motor fault detection system will be established through the comparison of the efficiency of these FS approaches.
Highlights
IntroductionAs the fourth industrial revolution emerges, the way factories work will never be the same
As the fourth industrial revolution emerges, the way factories work will never be the same.Factory automation has benefits such as economizing manpower, avoiding malfunction, as well as smart machines analyzing and diagnosing issues without human intervention
This study study uses artificial neural network training totoclassify classify and recognize thethe four cases of of induction motor failure with the features that were selected by feature selection approaches
Summary
As the fourth industrial revolution emerges, the way factories work will never be the same. This study analyzes motor failure in different conditions, including a functional motor, bearing failure, stator winding failure, and rotor failure. (4) Gabor-Winger transform (GWT) solves the problems of Gabor transform and Winger distribution function, yet GWT is not used in this study as it cannot analyze other bandwidths in the frequency domain. This study uses MRA to analyze the current signal to establish the FD system. According to [14], through feature engineering, design feature sets could improve the effect of recognition systems and reduce operating costs. This study uses artificial neural networks (ANN) to establish recognition systems with features that are selected with feature selection approaches. As the induction motor current signal is limited, this study uses ANN as a recognition system and stochastic gradient descent (SGD) [25] to calculate the error and fix the network of each sample. The time of training will be longer, but the effect will be better if the parameters are properly adjusted in neural network
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