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Open AccessArticle

Condition Monitor System for Rotation Machine by CNN with Recurrence Plot

Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan
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Author to whom correspondence should be addressed.
Energies 2019, 12(17), 3221; https://doi.org/10.3390/en12173221
Received: 2 August 2019 / Revised: 18 August 2019 / Accepted: 19 August 2019 / Published: 21 August 2019
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2019)
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. View Full-Text
Keywords: induction motor; convolutional neural networks (CNN); recurrence plots (RP); time-series data (TSD) induction motor; convolutional neural networks (CNN); recurrence plots (RP); time-series data (TSD)
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Hsueh, Y.; Ittangihala, V.R.; Wu, W.-B.; Chang, H.-C.; Kuo, C.-C. Condition Monitor System for Rotation Machine by CNN with Recurrence Plot. Energies 2019, 12, 3221.

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