Condition Monitor System for Rotation Machine by CNN with Recurrence Plot

Yumin Hsueh, Veeresha Ramesha Ittangihala, Wei-Bin Wu, Hong-Chan Chang and Cheng-Chien Kuo
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Yumin Hsueh: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan
Veeresha Ramesha Ittangihala: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan
Wei-Bin Wu: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan
Hong-Chan Chang: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan
Cheng-Chien Kuo: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan

Energies, 2019, vol. 12, issue 17, 1-13

Abstract: Induction motors face various stresses under operating conditions leading to some failure modes. Hence, health monitoring for motors becomes essential. In this paper, we introduce an effective framework for fault diagnosis of 3-phase induction motors. The proposed framework mainly consists of two parts. The first part explains the preprocessing method, in which the time-series data signals are converted into two-dimensional (2D) images. The preprocessing method generates recurrence plots (RP), which represent the transformation of time-series data such as 3-phase current signals into 2D texture images. The second part of the paper explains how the proposed convolutional neural network (CNN) extracts the robust features to diagnose the induction motor’s fault conditions by classifying the images. The generated RP images are considered as input for the proposed CNN in the texture image recognition task. The proposed framework is tested on the dataset collected from different 3-phase induction motors working with different failure modes. The experimental results of the proposed framework show its competitive performance over traditional methodologies and other machine learning methods.

Keywords: induction motor; convolutional neural networks (CNN); recurrence plots (RP); time-series data (TSD) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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