针对强噪声环境下旋转机械复合故障信号难于提取与分离的问题，提出了基于最大相关峭度解卷积(Maximum Correlated Kurtosis Deconvolution，MCKD)和重分配小波尺度谱的旋转机械故障诊断方法。机械信号中存在的噪声会降低重分配小波尺度谱的时频分布可读性，故先要对信号进行MCKD降噪，同时从振动信号中分离出各个故障成分，然后进行Hilbert变换得到包络成分，最后再对包络成分进行重分配小波尺度谱分析，根据尺度图中冲击成分的周期诊断转机械复合故障，算法仿真和应用实例验证了该方法的有效性。
Abstract
Aiming at the problem that the rotating machinery composite faults signal is difficult to be extracted and segmented from strong noise background，a fault diagnosis method for rotating machinery based on maximum correlated kurtosis deconvolution (MCKD) and reassigned wavelet scalogram was proposed. The noise in the rotating machinery vibration signal would reduced readability of its time-frequency representation，so the noise should be reduced by using MCKD，and the fault components were separated from the vibration signal, and then envelopes were obtained by Hilbert transform, finally envelopes were analyzed with the reassigned wavelet scalogram and the composite faults of rotating machinery were diagnosed according to the periods of impulse components in the scalogram, algorithm simulation and application examples validate the method.
关键词
最大相关峭度解卷积 /
重分配小波尺度谱 /
复合故障 /
最小熵解卷积
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Key words
maximum correlated kurtosis deconvolution /
reassigned wavelet scalogram /
composite fault /
minimum entropy deconvolution
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