改进层次基本尺度熵在液压泵故障诊断中的应用

Application of Modified Hierarchical Base Scale Entropy in the Fault Diagnosis of Hydraulic Pump

基于样本熵定义的多尺度熵在量化复杂信号时存在较大偏差,且其采用的粗粒化方法无法有效分析振动信号的高频成分,故障信息的利用程度较低。为解决此问题,提出一种新的振动信号不规则度量化方法——改进层次基本尺度熵(MHBSE)方案。MHBSE通过对时间序列进行层次符号化处理,不仅能够克服样本熵对复杂信号分析不足的缺陷,而且能够充分利用振动信号高频分量中的信息提高特征质量。鉴于MHBSE所具有的优异性能,提出一种新的液压泵健康状况检测方法。利用采集的液压泵振动实验数据对该方法进行有效性检验,实验结果证明,提出方法能够充分提取液压泵振动信号中的故障信息,且所提取的特征能够很好地表征液压泵的不同状态,最终故障识别率达到100%。

The multi-scale entropy theory based on the definition of sample entropy has a large deviation when quantifying complex signals,and the coarse-grained method it adopts cannot effectively analyze the high-frequency components of vibration signals,and the utilization of fault information is low.Aiming at the above problems,a new method for quantifying vibration signal irregularities is proposed-modified hierarchical base-scale entropy(MHBSE).MHBSE can not only overcome the shortcomings of sample entropy in the analysis of complex signals by performing hierarchical symbolization on time series,but also make full use of the faults information in the high frequency components of vibration signals,thereby improving the quality of features.In view of the excellent performance of MHBSE,a new method for detecting the health of hydraulic pumps is proposed.The effectiveness of the proposed method is tested using the collected hydraulic pump vibration experimental data.The experimental results show that the proposed method can fully extract the fault information in the hydraulic pump vibration signal,the extracted features can well characterize the different states of hydraulic pump,and the final fault recognition rate reached 100%.