Vibration Signal Noise-Reduction Method of Slewing Bearings Based on the Hybrid Reinforcement Chameleon Swarm Algorithm, Variate Mode Decomposition, and Wavelet Threshold (HRCSA-VMD-WT) Integrated Model

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 11; p. 3344
• Main Authors: Li, Zhuang, Yao, Xingtian, Zhang, Cheng, Qian, Yongming, Zhang, Yue
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.05.2024; MDPI
• Subjects: Accuracy; Algorithms; Chameleon Swarm Algorithm (CSA); Fault diagnosis; Mathematical optimization; Methods; Mutation; Neural networks; Noise control; noise reduction; Optimization algorithms; slewing bearing; Support vector machines; Variate Mode Decomposition (VMD); Vibration analysis; vibration signal; Wavelet Threshold (WT) denoising; United Kingdom; noise reduction; Chameleon Swarm Algorithm (CSA); Variate Mode Decomposition (VMD); Wavelet Threshold (WT) denoising; vibration signal; slewing bearing
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24113344

To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic Reverse Learning (CRL), the Whale Optimization Algorithm’s (WOA) bubble-net hunting, and the greedy strategy with the Cauchy mutation to diversify the initial population, accelerate convergence, and prevent local optimum entrapment. Furthermore, by optimizing Variate Mode Decomposition (VMD) input parameters with HRCSA, Intrinsic Mode Function (IMF) components are extracted and categorized into noisy and pure signals using cosine similarity. Subsequently, the Wavelet Threshold (WT) denoising targets the noisy IMFs before reconstructing the vibration signal from purified IMFs, achieving significant noise reduction. Comparative experiments demonstrate HRCSA’s superiority over Particle Swarm Optimization (PSO), WOA, and Gray Wolf Optimization (GWO) regarding convergence speed and precision. Notably, HRCSA-VMD-WT increases the Signal-to-Noise Ratio (SNR) by a minimum of 74.9% and reduces the Root Mean Square Error (RMSE) by at least 41.2% when compared to both CSA-VMD-WT and Empirical Mode Decomposition with Wavelet Transform (EMD-WT). This study improves fault detection accuracy and efficiency in vibration signals and offers a dependable and effective diagnostic solution for slewing bearing maintenance.