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http://dx.doi.org/10.9717/kmms.2017.20.11.1811

Fault Diagnosis of Induction Motor using Linear Predictive Coding and Deep Neural Network  

Ryu, Jin Won (School of IT Convergence, University of Ulsan)
Park, Min Su (School of IT Convergence, University of Ulsan)
Kim, Nam Kyu (School of IT Convergence, University of Ulsan)
Chong, Ui Pil (School of IT Convergence, University of Ulsan)
Lee, Jung Chul (School of IT Convergence, University of Ulsan)
Publication Information
Abstract
As the induction motor is the core production equipment of the industry, it is necessary to construct a fault prediction and diagnosis system through continuous monitoring. Many researches have been conducted on motor fault diagnosis algorithm based on signal processing techniques using Fourier transform, neural networks, and fuzzy inference techniques. In this paper, we propose a fault diagnosis method of induction motor using LPC and DNN. To evaluate the performance of the proposed method, the fault diagnosis was carried out using the vibration data of the induction motor in steady state and simulated various fault conditions. Experimental results show that the learning time of our proposed method and the conventional spectrum+DNN method is 139 seconds and 974 seconds each executed on the experimental PC, and our method reduces execution time by 1/8 compared with conventional method. And the success rate of the proposed method is 98.08%, which is similar to 99.54% of the conventional method.
Keywords
Induction Motor; Diagnosis; Spectrum; LPC; DNN;
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Times Cited By KSCI : 2  (Citation Analysis)
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