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Title: A learning-based method for speed sensor fault diagnosis of induction motor drive systems
Authors: Xia, Yang
Xu, Yan
Gou, Bin
Deng, Qingli
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Xia, Y., Xu, Y., Gou, B. & Deng, Q. (2021). A learning-based method for speed sensor fault diagnosis of induction motor drive systems. IEEE Transactions On Instrumentation and Measurement, 71, 3504410-.
Journal: IEEE Transactions on Instrumentation and Measurement
Abstract: This article proposes a speed sensor fault diagnosis methodology based on a learning-based data-driven principle in induction motor drive systems. The proposed method is derived from signal estimation and residual evaluation. First, a speed estimator is designed with a nonlinear autoregressive exogenous (NARX) learning model and a randomized learning technique called random vector functional link (RVFL) network. A data preprocessing method by discrete wavelet transform (DWT) is applied to better trace the signal trends, in order to further improve the speed estimation accuracy. After the estimation, the residual between the measured and estimated signals can be obtained, and a decision-making mechanism is developed for fault diagnosis based on an outlier test. The offline test results show that the proposed method can accurately estimate the speed signal with a 1.554e -4 root-mean-square error (RMSE) and outperforms the state-of-the-art methods. Moreover, real-time tests are also carried out to verify the feasibility and stability during the online stage. Moreover, the proposed approach does not require any motor parameters and other additional hardware, which makes it quite suitable for online practical applications.
ISSN: 0018-9456
DOI: 10.1109/TIM.2021.3132053
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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