滚动轴承局部故障产生的循环冲击由轴承内部到达传感器的过程中受到传递路径、环境噪声和偶然性冲击干扰等因素影响，从而导致故障特征提取困难，诊断效果不佳。提出一种基于最大相关峭度解卷积和可调品质因子小波变换相结合的轴承故障复合诊断方法，前者用于削弱传递路径影响，后者通过滤波抑制噪声成分。两者参数优化过程中一致采用能够考虑滚动轴承故障冲击周期发生特点的相关峭度为优化指标，以保证特征提取的总体效果，同时该指标不受偶然性冲击干扰影响。对仿真和实验室信号进行分析，并与快速谱峭度等常用方法进行对比，结果验证了该方法在滚动轴承故障诊断中的有效性和优越性。
Abstract
Cycle impact caused by rolling bearing local fault transmitted from bearing to sensor is affected by transmission path, environmental noise and accidental impact interference, which makes fault feature extraction difficult and diagnosis effect poor.Here, a composite fault diagnosis method of rolling bearing based on the maximum correlation kurtosis deconvolution and adjustable quality factor wavelet transform was proposed.The former was used to weaken the influence of transfer path, while the latter was used to suppress noise components with filtering, and their parameter optimizations consistently took the correlated kurtosis, which could consider characteristics of rolling bearing fault impact cycle, as the optimization index to ensure the overall effect of feature extraction.Meanwhile, this index could not be affected by accidental impact interference.Simulated and test signals were analyzed using the proposed method, and the results were compared to those using the common methods, such as, fast spectral kurtosis to verify the effectiveness and superiority of the proposed method in rolling bearing fault diagnosis.
关键词
故障诊断 /
相关峭度 /
小波变换 /
特征增强
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Key words
fault diagnosis /
correlated kurtosis /
wavelet transform /
feature enhancement
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