A novel nonlinear observer for fault diagnosis of induction motor

In order to accurately diagnose the fault of induction motor, a fault diagnosis of nonlinear observer method based on BP neural network and Cuckoo Search algorithm is proposed. It is a new method which mixes analytical model and artificial neural network; firstly, the induction motor model is divide...

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Bibliographic Details
Published in:Journal of algorithms & computational technology 2020-05, Vol.14
Main Authors: Yi, Lingzhi, Liu, Yue, Yu, Wenxin, Zhao, Jian
Format: Article
Language:English
Subjects:
Artificial neural networks
Fault detection
Fault diagnosis
Induction motors
Mathematical models
Neural networks
Search algorithms
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Summary:In order to accurately diagnose the fault of induction motor, a fault diagnosis of nonlinear observer method based on BP neural network and Cuckoo Search algorithm is proposed. It is a new method which mixes analytical model and artificial neural network; firstly, the induction motor model is divided into linear and nonlinear parts, and BP neural network is used to approximate the nonlinear part. Then an adaptive observer is established, in which a simple and effective method for selecting the feedback gain matrix is offered. Cuckoo Search algorithm is utilized to improve the convergence speed and approximation accuracy in BP Neural Network. Compared with some other algorithms, the simulation results show that the proposed method has higher prediction accuracy. The designed nonlinear observer can estimate the current and speed accurately. Finally, the experiment of winding fault is implemented, and the online fault detection of induction motor is realized by analyzing the current residual errors.
ISSN:1748-3026
1748-3018
1748-3026
DOI:10.1177/1748302620922723