A Learning-Based Method for Speed Sensor Fault Diagnosis of Induction Motor Drive Systems

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.

Automated summary

This article proposes a speed sensor fault diagnosis methodology in induction motor drive systems. The method utilizes a learning-based data-driven principle and involves signal estimation, residual evaluation, and decision-making based on outlier test. The speed estimation is achieved through a nonlinear autoregressive exogenous (NARX) learning model and a randomized learning technique called random vector functional link (RVFL) network. The proposed approach shows promising performance in offline and real-time tests, without requiring motor parameters or additional hardware.