Optimal Legendre multiwavelet frequency band-based an improved adaptive denoising algorithm for mechanical fault diagnosis under complex conditions

Abstract

Conventional fault diagnosis methods face significant challenges in real-world engineering scenarios, including heavy background noise, wrong labels, and limited fault samples. To address these challenges, this paper proposes an improved adaptive denoising algorithm combining Legendre multiwavelet with genetic algorithm (IAD-LWGA). This novel method devises a modified threshold estimation algorithm on each LW frequency band optimized by GA, resulting in effectively suppressing noise while preserving fault-related features. The efficacy of the proposed approach is first validated on a real-world air conditioner external unit dataset. Subsequently, the extracted optimal feature combinations are directly transferred to PHM 2009 gearbox compound fault diagnosis dataset, demonstrating strong cross-domain generalization ability with minimal requirement for domain-specific expertise. Extensive experiments show that the proposed approach consistently outperforms state-of-the-art models, attaining 100 % accuracy for both datasets under normal conditions, while reaching 100 %, 94.75 %, 93.57 % accuracies with –10 dB noise, label noise ratio 0.05, faulty samples 10 for Dataset 1, and achieving 99.83 %, 95.33 %, 90.80 % accuracies with 6 dB noise, label noise ratio 0.05, limited faulty samples 10 for Dataset 2, respectively. This work presents a practical and effective strategy for fault diagnosis in complex industrial environments, enhancing predictive maintenance capabilities of expert and intelligent systems.