基于模糊熵与CS-ELM的供输弹系统早期故障识别

针对供输弹系统早期采集的信号中成分复杂,故障特征难以提取和识别的问题,提出一种基于模糊熵与布谷鸟改进的极限学习机(CS-ELM)的供输弹系统早期故障预示方法。运用改进的可调品质因子小波变换对信号进行滤波降噪,提取各子带信号的模糊熵特征;选取模糊熵值较大的5个子带进行重构,完成降噪并将其模糊熵组成特征向量;运用CS-ELM对所提取的特征向量进行早期故障预示并与ELM的诊断结果进行对比。试验结果验证了该方法的有效性,其预示准确率达90.7%。

Early Fault Recognition of the Bomb Supply and Transport System Based on Fuzzy Entropy and CS-ELM

For the complex components in the signals collected by the bomb supply and transport system in the early stage,and difficulty in extracting and identifying fault characteristics,a kind of early fault prediction method of the bomb supply and transport system based on fuzzy entropy and cuckoo improved extreme learning machine (CS-ELM) was proposed.The improved adjustable quality factor wavelet transform was used to filter the signal and the fuzzy entropy characteristics of each sub-band signal was extracted;the five sub-bands with larger fuzzy entropy value were selected to reconstruct to complete the noise reduction and their fuzzy entropy was incorporated into feature vectors;CS-ELM was used to predict the early failure of the extracted feature vector and the result was compared with the diagnosis result of ELM.The experimental results show that the proposed method is effective,and the prediction accuracy is 90.7%.