Decision-level fusion diagnosis method of reciprocating compressor facing multi-source uncertain data
In order to solve the problem that the accuracy of multi-source information fusion diagnosis model is reduced due to high uncertain data sources,a decision fusion diagnosis method of reciprocating compressor facing uncertain data was pro-posed.A preliminary diagnosis model based on the GRU-AlexNet network was constructed to obtain the initial diagnosis re-sults of each sensor signal of the reciprocating compressor,then the concepts of cosine similarity and confidence entropy were introduced to build a joint index to improve the traditional DS evidence theory,and the multi-source signal decision fusion di-agnosis was carried out combining with the preliminary diagnosis results.The results show that in the experimental study on the acceleration,displacement,and pressure signal(uncertain data)fusion diagnosis of reciprocating compressor faults,the fusion diagnosis accuracy was 99.98％,which was 9.27,5.13,and 48.30 percentage points higher than the single signal source diagnosis results,respectively.This method greatly reduces the influence of uncertain information on the fusion diagno-sis results,and has good fault tolerance and stability.It can effectively improve the accuracy of various types of fault identifi-cation,thereby improving the stability of equipment and ensuring its good working condition.It is of great significance to en-sure the work safety of enterprises and improve the output capacity of equipment.
reciprocating compressorintelligent diagnosisuncertain datamulti-source information fusionDS evidence theory
万方数据
维普
