MED结合分层模糊熵在刮板输送机故障识别中的应用

对刮板输送机进行故障识别可保障刮板输送机实现快速故障定位与维修,减少故障维修带来的经济损失。对刮板输送机故障类型进行分类,以表征各个易损部件状态的参数作为故障诊断的输入信号,构建基于最小熵解卷积(MED)和分层模糊熵的刮板输送机故障诊断模型。分析基于MED结合分层模糊熵的刮板输送机故障诊断流程,对比采用MED、分层模糊熵和MED结合分层模糊熵算法在不同样本集中的准确率。结果表明,采用MED结合分层模糊熵得到的诊断结果准确率更高且更为稳定,在复杂结构的故障诊断中表现出明显的优势。

Application of MED Combined with Hierarchical Fuzzy Entropy in Fault Identification of Scraper Conveyor

Fault identification of the scraper conveyor can ensure that the scraper conveyor can quickly locate and repair the fault,and reduce the economic loss caused by the fault repair.The fault types of scraper conveyors were classified and the parameters that characterize the status of each vulnerable component were taken as the input signal of fault diagnosis.A scraper conveyor fault diagnosis model has been built based on minimum entropy deconvolution(MED)and hierarchical fuzzy entropy.The fault diagnosis process of scraper conveyor based on MED combined with hierarchical fuzzy entropy was analyzed and the accuracy of MED,hierarchical fuzzy entropy and MED combined with hierarchical fuzzy entropy algorithm were compared in different sample sets.The results show that the diagnostic results obtained by MED combined with hierarchical fuzzy entropy is more accurate and stable,and show obvious advantages in the fault diagnosis in complex structures.