Aero-engine is a kind of mechanical equipment with possible multi-fault risk.The application of advanced computing training method can effectively realize accurate risk early warning analysis,and provide reference for the guidance of engine operation and maintenance.Multivariable time series samples were extracted from the early warning symptom data set of engine failure risk,and the samples were matrix-transformed into gray scale samples.Image samples were preprocessed and enhanced,and sequence sample tags were thermally encoded.Deep attention mechanism and residual shrinkage block with threshold were integrated into the deep residual shrinkage network(DRSN),so as to obtain high discriminant features and realize soft thresholding.Combining long short term memory layers with multiple hidden layers,DRSN model was improved,and principal component analysis was made to reconstruct features and extract principal components.The cumulative interpretable variance contribution rate was 93.7％.The training accuracy for identifying,classifying,and warning 20 potential fault symptoms was 96.1％.An improved early warning DRSN model of engine fault risk was proposed.Compared with other algorithms,this model of strong robustness improved the accuracy by at least 4.4％.
维普
万方数据