Aero-engine service life prediction method based on label-free, unbalanced and initial value uncertain data

Abstract

The invention discloses an aero-engine service life prediction method based on label-free, unbalanced and initial value uncertain data, which comprises the following steps of firstly, carrying out feature screening on an engine training data set by utilizing a correlation index and a trend index; then, obtaining a health state label through quantum fuzzy clustering, training a multivariable deep forest classifier, and obtaining an aero-engine health assessment model; meanwhile, training a long-short cycle memory neural network (LSTM) time sequence prediction model by utilizing the engine training data set; and finally, obtaining the maintenance time and the final residual service life (RUL) of the engine at different health stages by using the engine test data set according to the trainedhealth assessment model and the time sequence prediction model. According to the method, the defects of no label, imbalance and initial value uncertainty of observation data are overcome, and a technical reference is provided for maintenance decisions of the aero-engine in different subsequent health stages.

Description

technical field [0001] The invention relates to an aero-engine life prediction method, in particular to an aero-engine life prediction method based on unlabeled, unbalanced and uncertain initial value data. Background technique [0002] The engine is the most sophisticated and complex subsystem in the aircraft. It provides the power for the aircraft to fly, and has extremely strict requirements on safety and reliability. During the long-term continuous operation of the engine in an extreme environment, its performance will inevitably degrade, which can easily cause various failures and threaten the safety of the system. The PHM technology with health assessment and failure prediction as the core is the key to realize the predictive maintenance of aeroengine and ensure the reliability of engine operation, and has become the focus of attention in the aviation field. In recent years, due to the promotion of big data analysis technology, cloud computing technology and artificia...

AI Technical Summary

Problems solved by technology

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to provide an aero-engine life prediction method based on unlabeled, unbalanced, and uncertain initial values. In the case of deterministic problems, it solves the problem that the existing multivariate methods cannot make good predictions based on the above observation data, and can realize the maintenance time and RUL prediction of different health stages of aeroengines in the absence of prior knowledge. Provide technical reference for maintenance decision-making in different health stages of aero-engines

Images