Feature extraction for early fault detection in rotating machinery of nuclear power plants based on adaptive VMD and Teager energy operator

• DOI: 10.1016/j.anucene.2021.108392
• Publication Date: 2021-05-28T07:07:30Z
• Authors: Shaomin Zhu; Hong Xia; Binsen Peng; Enrico Zio; Zhichao Wang; Yingying Jiang
• Keywords: Rotating machinery; Teager energy operator; Artificial bee colony; Early fault feature extraction; 0202 electrical engineering, electronic engineering, information engineering; Parameter-adaptive VMD; 02 engineering and technology; [SHS.GEST-RISQ]Humanities and Social Sciences/domain_shs.gest-risq; 7. Clean energy; Nuclear power plant; 004

Abstract Extracting features for early failure detection in rotating machinery of nuclear power plants (NPPs) is difficult because in the early stages of failure the impact on the vibration signals is weak. To improve early fault detection in rotating machinery, a fault feature extraction method based on the combination of parameter-adaptive Variational Mode Decomposition (VMD) and Teager energy operator (TEO) is proposed in this paper. Firstly, we introduce the maximum weighted kurtosis index (WKI) as the objective function, and the Artificial Bee Colony (ABC) is used to optimize the VMD parameters. Then, the optimized VMD is used to decompose the vibration signal into multiple intrinsic mode functions (IMFs). Finally, TEO is used to demodulate the sensitive mode with the maximum WKI and identify the fault frequencies. Simulation and experiment show that the early fault features in vibration signals can be effectively extracted by the proposed method, and the comparisons with other three methods highlight the advantages of the proposed method.