Weak fault feature extraction for polycrystalline diamond compact bit based on ensemble empirical mode decomposition and adaptive stochastic resonance. (June 2021)

• Record Type: Journal Article
• Authors: Gao, Kangping; Xu, Xinxin; Li, Jiabo; Jiao, Shengjie; Shi, Ning
• Is Part Of: Measurement. Volume 178(2021)
• Publication Date: 2021-06
• Subjects: Weak signal detection -- Feature extraction -- Adaptive stochastic resonance -- Ensemble empirical mode decomposition -- Gray wolf optimization algorithm -- Polycrystalline diamond compact bit; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109304
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A weak fault signal extraction method of rotating machinery is proposed. Defines a new ASR performance evaluation index-weighted power spectrum kurtosis. GWO is used to optimize the structural parameters of SR. Simulation analysis verifies the effectiveness of the proposed method. Two cases studies further demonstrate the feasibility of proposed method in engineering application. Abstract: Aiming at the problem of weak early fault signals of rotating machinery, a feature extraction method combining ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance (ASR) is proposed. First, the original vibration signal is decomposed into a series of intrinsic mode functions (IMF) through EEMD, and the main IMF components are selected using the correlation coefficient and the root mean square principle. Next, the selected components are reconstructed and used as the input of the ASR system based on the gray wolf algorithm. Finally, to evaluate the performance of ASR, a new evaluation index-weighted power spectrum kurtosis (WPSK) is defined. The results of experimental analysis on the simulation signals and vibration signals are used to verify the feasibility and superiority of the proposed method. Compared with the GA-SR, the proposed method increases the WPSK value by 34.5% when detecting weak signals of worn polycrystalline diamond compact bits.