基于多尺度样本熵和VPMCD的自动机故障诊断

针对自动机故障诊断过程中振动信号的非线性、非平稳性、非周期性导致的故障特征较难提取,以及故障识别率偏低这一问题,提出了一种基于多尺度样本熵和多变量预测模型(variable predictive model-based class discriminate,简称VPMCD)的自动机故障诊断方法。首先,对采集到的信号进行小波阈值降噪处理;其次,利用小波包分解的方法对振动信号进行分解,得到多个尺度下的信号分量;然后,计算不同尺度下信号的样本熵值,并提取对故障特征较为敏感的尺度因子,组成故障特征向量;最后,利用多变量预测模型对故障特征向量进行训练和识别,进而实现自动机的故障诊断。自动机故障诊断试验分析结果表明,利用多尺度样本熵和多变量预测模型的方法可以准确识别多种典型的自动机故障类型。

Fault Diagnosis Method for Automata Machine Based on Multiple Scale Sample Entropy and VPMCD

As the characteristics of presents the fault features of vibration signals are hard to extract due to its nonlinearity, no-stationary and aperiodicity.In the light of the low fault recognition rate in an automata machine for this reason, a new approach is proposed which combines multiple scale sample entropy and variable predictive model based on the class discriminate (VPMCD). First, wavelet threshold noise reduction is applied to the collected signals. Then, the vibration signals are decomposed into several signals in different scales, the sample entropy of different signals are calculated and the sensitivity to fault characteristics are selected to constitute the fault features. Finally, the VPMCD is establishedbased on the fault features to recognize and classify the automata faults. The experimental results show that this method can accurately distinguish several typical fault forms of automata.