关键词: 往复式压缩机, 故障特征提取, 滑动轴承, 多尺度样本熵

Abstract: Aiming at the difficulties in fault vibration signal feature extraction and low recognition accuracy of reciprocating compressor plain bearing,an improved Mutiscale Entropy (MSE) bearing fault feature extraction method was proposed. Firstly,in order to solve the problem of redundant calculation and large amount of calculation in the calculation step of sample entropy,the symbolic idea was introduced to simplify the repeated calculation problem when counting the number of vectors whose distance between vectors was less than a threshold under different dimensions,and a fast sample entropy algorithm was obtained. Secondly,in view of the fact that the traditional cubic spline interpolation method cannot meet the complex and variable time series signal multi-scale processing,cubic triangular B-spline interpolation was proposed to replace the traditional cubic spline interpolation to multiscale the multi-scale sample entropy and improve the accuracy of MSE. The previous complex compressor sliding bearing clearance fault was the research object,and the improved MSE method was used to extract the fault signal feature. The research results showed that when the sample length was 24 056,the computational efficiency of the improved MSE algorithm feature extraction increases by nearly 9.12 times,and the percentage of increase was 816.62 %. At the same time,this method improved the accuracy of fault diagnosis and recognition of the original MSE method.

Key words: reciprocating compressor, fault feature extraction, sliding bearing, multiscale entropy