基于自适应时频分解的瞬态冲击信号提纯

Transient Impulse Signal Extraction Based on Self-adaptive Time-frequency Decomposition

在分析非平稳振动信号中瞬态冲击信号和平稳振动信号各自特点的基础上,发展了新的提纯机械故障冲击成分的信号处理方法。首先选择Chirplet基函数将被分析信号自适应展开,得到噪声抑制,高分辨率且无交叉干扰项的自适应时频谱,然后分别使用了两种方法从自适应时频谱中恢复出瞬态冲击分量。一种是采用振动冲击信号模型的时频分布去逼近时频滤波后的冲击分量时频分布,继而借助信号模型重构出冲击分量;另一种是根据冲击信号的Chirplet基函数的参数表达特征,直接选择所需基函数对冲击分量进行重构。最后,采用这两种方法分别对齿轮和轴承的故障信号进行分析,分析结果验证了这两种方法对瞬态冲击信号提取都非常有效,在机械故障诊断中有很大的参考价值。

Based on the analysis of characteristics of fault signals generated by mechanisms and stationary vibration signal, a novel method for extracting impulse components from vibration signals is presented. The original signal is decompose into a linear combination of Chirplet functions that are well concentrated both in time and frequency, aiming to get self-adaptive time-frequency （TF） spectra with high resolution and without noise and cross-terms. Then two methods are used to extract transient impulse components. One is using the TF of the vibration impulse signal model to approximate the TF distribution signal being filtered out to determine the reconstructed impulse signal model. The other method is using Chirplet elementary function to reconstruct directly based on the Chirplet expression characteristics of the transient impulse component. The fault signals of gear and beating are analyzed by using the two methods respectively. The analysis results verify that the two methods are effective for the impulse signals and have great reference values in mechanical fault diagnosis.