Order spectrum analysis enhanced by surrogate test and Vold-Kalman filtering for rotating machinery fault diagnosis under time-varying speed conditions
Order spectrum analysis enhanced by surrogate test and Vold-Kalman filtering for rotating machinery fault diagnosis under time-varying speed conditions
复制标题
代理试验和Vold-Kalman滤波增强阶次谱分析用于时变速度条件下旋转机械故障诊断
DOI：
10.1016/j.ymssp.2020.107585
复制
发表时间：
2021-06
影响因子：
8.4
通讯作者：
Feng Zhipeng
中科院分区：
文献类型：
--
作者：
Chen Xiaowang;Feng Zhipeng
旋转机械信号通常由旋转频率谐波主导，并且这些频率分量的存在或其幅度的变化指示健康状况。阶次谱因其能直观地在阶次域中表征旋转机械的旋转频率谐波，在旋转机械故障特征提取中得到了广泛应用。然而，一些速度非同步分量经常存在。它们在阶谱中表现出宽带特征，阻碍了旋转频率谐波阶次的准确识别。为了解决这个问题，提出了一种方案，以识别和消除速度非同步成分的恒定或时变频率。为此，通过新的候选构造方法和基于非平稳性的指标作为检验准则，推广了替代检验，以自适应地识别真正的非平稳信号分量，消除主观影响。利用Vold-Kalman滤波器对复杂非平稳信号进行单分量分解的能力，将其用于分离信号分量。通过对感应电机定子电流信号和水轮机涡轮机转子振动信号的分析，验证了该方法的有效性。结果表明，它优于传统的计算阶次谱分析。
Rotating machinery signals are usually dominated by rotating frequency harmonics, and the presence of these frequency components or change in their magnitudes indicate health condition. Order spectrum is widely applied in rotating machinery fault feature extraction, because of its capability in intuitive spectral representation of rotating frequency harmonics in order domain. However, some speed non-synchronous components are often present. They show wide-band features in order spectra, and hinder accurate identification of rotating frequency harmonic orders. To address this issue, a scheme is proposed to identify and remove speed non-synchronous components of either constant or time-varying frequency. To this end, surrogate test is generalized through new candidate construction method and nonstationarity based indicator for test criterion, to identify true nonstationary signal components adaptively and eliminate subjective influences. Vold-Kalman filter is used to separate signal components by exploiting its capability in mono-component decomposition of complex nonstationary signals. The proposed method is validated through analyses of both induction motor stator current signals and hydraulic turbine rotor vibration signal. The results demonstrate its advantages over conventional computed order spectrum analysis.
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