噪声与振动控制2017，Vol.37Issue(5)：175-179,5.DOI:10.3969/j.issn.1006-1355.2017.05.036
遗传算法优化稀疏分解的齿轮箱故障诊断研究
Research on Fault Diagnosis of Gearboxes Based on Genetic Algorithm Optimized Sparse Decomposition
摘要
Abstract
Transmission system of gearbox has a very complex structure and its faulty vibration signals always include strong noise. Feature extraction of weak signals from the background of strong noise is a difficult problem in vibration signal processing. Sparse decomposition method can extract the weak signal features adaptively under strong noise background, but it needs large computer time consuming in searching for the optimal matching atoms. To speed up the matching process for the optimal atoms, the signal sparse decomposition algorithm using genetic algorithm to optimize matching pursuit and tracking is proposed. Results show that the optimized algorithm can greatly reduce the computation time for searching for the optimal atom parameters in matching and tracking algorithm, and the main feature of gear's faulty vibration signal is the modulation phenomenon which can reduce the noise in the signal through sparse decomposition. Then, the frequency domain analysis is made and the fault diagnosis for gears is realized according to the frequency domain analysis results. Comparative analysis of the modulated vibration signal of the simulated gear with the actually collected gearbox vibration signal has shown that this method can extract fault feature frequency from the vibration signal with strong noise quickly and accurately.关键词
振动与波/稀疏分解/匹配追踪算法/遗传算法/齿轮箱/故障诊断Key words
vibration and wave/sparse decomposition/matching pursuit algorithm/genetic algorithm/gearbox/fault diagnosis分类
机械制造引用本文复制引用
宋昌浩,纪国宜..遗传算法优化稀疏分解的齿轮箱故障诊断研究[J].噪声与振动控制,2017,37(5):175-179,5.基金项目
江苏省高校优势学科建设工程基金资助项目 （）
