Engine life prediction method, storage medium and computing equipment

Abstract

The invention discloses an engine life prediction method, a storage medium and computing equipment, and the method comprises the steps: removing the information redundancy features in engine state data according to the correlation degree between the features, and removing the features with the small correlation degree with a prediction target according to the correlation between the features and the prediction target; randomly selecting a sampling sample set, randomly selecting a feature subspace on the random sampling sample set, establishing a decision regression tree on the obtained randomsampling subspace, and obtaining a life prediction result under a corresponding feature combination by the decision trees on different random sampling subspaces; constructing an MLP model structure and a loss function, and obtaining MLP model parameters through Adam algorithm learning; and integrating prediction results of the decision trees based on the trained MLP model to obtain the remaining service life of the engine. According to the method, prediction values of decision trees in different random sampling subspaces are integrated through a learning method, the prediction accuracy and reliability are improved, and a basis is provided for maintenance and fault prediction of the aero-engine.

Description

technical field [0001] The invention belongs to the technical field of remaining life prediction of aviation turbofan engines, and in particular relates to an engine life prediction method based on MLP integrated random sampling subspace decision tree, a storage medium and a computing device. Background technique [0002] Correctly predicting the remaining life of an aeroengine is an important means to maintain the engine reasonably and ensure the safety of the aircraft. Numerous parameters related to aeroengines can reflect the working state and remaining life of the engine. At present, with the wide application of intelligent instruments and computer storage technology, a large number of parameter data can be effectively monitored, collected and stored. Mining based on the collected data has become one of the effective ways and means to predict the remaining life of the engine. This data-driven method does not need to analyze the fault state and the working mechanism bet...

AI Technical Summary

Problems solved by technology

[0004] Previous research work applied certain methods and technical means to realize the prediction of the remaining service life of aero-engines, which promoted the research progress in this field, but there is no specific rule for the selection of attributes for data analysis and mining

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