Fault diagnosis of mine asynchronous motor based on MEEMD energy entropy and ANN. (June 2021)

• Record Type: Journal Article
• Authors: Yang, Zhanshe; Kong, Chenzai; Wang, Yunhao; Rong, Xiang; Wei, Lipeng
• Is Part Of: Computers & electrical engineering. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Rotor fault -- Empirical Mode Decomposition -- Energy entropy -- Artificial Neural Network -- ANSYS Maxwell; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107070
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The stator current signals of mining asynchronous motor are often non-stationary, making it challenging to extract fault features in the time domain. Therefore, this paper proposes a rotor fault diagnosis method based on the combination of Modified Ensemble Empirical Mode Decomposition (MEEMD) energy entropy and Artificial Neural Network (ANN). Firstly, the stator current signals are decomposed into a series of Intrinsic Mode Function (IMF) components by the MEEMD. Secondly, the IMF components with the most abundant information are selected by the cross-correlation criterion, and their energy entropy is calculated to construct feature vectors. Finally, the feature vectors are input into the ANN for training and state recognition. The faulty motor is modeled by ANSYS Maxwell software to obtain the simulated data. It is verified that the MEEMD-ANN method is feasible for fault diagnosis of mine motors, which can accurately identify the different status of motors, including normal state, broken rotor bars, and air gap eccentricity, the recognition rate can reach 99%. The MEEMD-ANN improves the accuracy by 2% compared with the EEMD-ANN, improves the accuracy by 3.75% compared with the MEEMD-SVM. Highlights: A motor fault diagnosis method based on MEEMD energy entropy and ANN was proposed. Used Ansys Maxwell software to establish a finite element faulty motor model. Realized the accurate recognition of asynchronous motor faults. Recognition rate of normal state, rotor bar broken and air gap eccentricity is 99%.