Weak Fault Feature Extraction and Enhancement of Autonomous Underwater Vehicle Thrusters Based on Artificial Rabbits Optimization and Variational Mode Decomposition

Abstract

Variational Mode Decomposition (VMD) has typically been used in weak fault feature extraction in recent years. The problem analyzed in this study is weak fault feature extraction and the enhancement of AUV thrusters based on Artificial Rabbits Optimization (ARO) and VMD. First, we introduce ARO to solve the problem of long-running times when using VMD for weak fault feature extraction. Then, we propose a VMD denoising method based on an improved ARO algorithm to address the issue of deteriorations in the fault feature extraction effect after introducing ARO. In this method, chaotic mapping and Gaussian mutation are used to improve ARO to optimize the parameters of VMD. This leads to a reduced running time and improved fault feature extraction performance. We then perform fault feature enhancement. Due to the unsatisfactory enhancement effect of traditional modified Bayes (MB) methods for weak fault features, we introduce energy operators to transform the fault signals into the energy domain for fault feature enhancement. Finally, we add differential processing to the signal to address the issue of certain fault feature values decreasing after introducing energy operators. In the end, the effectiveness of the proposed methods is verified via pool experiments on a “Beaver II” AUV prototype.

Keywords

• Autonomous Underwater Vehicle
• weak fault
• Artificial Rabbits Optimization
• fault feature extraction
• fault feature enhancement
• experiment study