Aero-engine service life prediction method based on improved LSTM

Abstract

The invention belongs to the technical field of aero-engines, and particularly relates to an aero-engine service life prediction method based on improved LSTM. Comprising the steps of 1, processing original sensor data acquired by a sensor, and constructing a training sample which comprises a training set and a test set; step 2, on the basis of constructing the training sample in the step 1, constructing an LSTM structure model as an engine residual life prediction model; and 3, inputting the test set in the step 1 into the LSTM structure model constructed in the step 2 to obtain a predicted RUL value, and evaluating the obtained predicted RUL value by adopting RMSE and Score evaluation indexes. According to the aero-engine life prediction method based on SDAE and LSTM, the advantage of unsupervised feature extraction of a deep encoder is utilized, effective feature extraction is performed on an engine sensor signal, low efficiency of manual feature extraction and prediction uncertainty caused by the low efficiency are avoided, and the prediction accuracy is improved. And the residual life of the engine is predicted by using the advantage of processing the time sequence data by the LSTM model.

Description

technical field [0001] The invention belongs to the technical field of aero-engines, in particular to an aero-engine life prediction method based on improved LSTM. Background technique [0002] Aeroengine is an important part of the normal flight of the aircraft. Due to the complex and changeable operating conditions of the engine and the relatively harsh operating environment, once it fails, it will pose a huge threat to flight safety and the safety of passengers. The remaining life prediction of aero-engines is based on condition monitoring data, such as fan, compressor inlet and outlet temperature, pressure, speed and other historical data, and features are extracted to build a life prediction model and provide technical support for preventive maintenance, which has a wide range of application values. [0003] In recent years, data-driven methods have gradually become the mainstream technology in the field of remaining life prediction. There are two types of commonly used...

AI Technical Summary

Problems solved by technology

Kernel principal component analysis (KPCA) can be used to remove information redundancy between features and construct a reduced feature matrix. The complexity of the algorithm is closely related to the dimension of the data. Since the monitoring data collected by the engine sensor It has the characteristics of high dimensionality, multi-parameters, and large scale, resulting in high complexity of the feature extraction model

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