Health status assessment and prediction method for a diesel engine air system based on time series
WANG Yongkang
WEN Guanhua
WANG Yanyan
SHEN Zhaojie
MA Conggan
JI Zhaoqi
LIN Bo
SHAO Luming
Abstract：To address the challenge of health status assessment for diesel engine air systems,when considering their complex structure,high fault frequency,insufficient full-life-cycle data,and difficulties in accurate fault characterization,and to provide support for predictive maintenance,a time-series-based health status assessment method for diesel engines is proposed.Initial health indicators are constructed according to the health status of the air system per unit time.An autoencoder model is adopted to perform feature weighting optimization on the initial indicators for improved characterization accuracy.Combined with the sliding window method,time-series analysis is applied to smooth historical health status data.Finally,a long short-term memory(LSTM)network is established to predict the health status of the diesel engine air system.The results demonstrate that the designed time-series-based assessment method is effective with high prediction accuracy.The mean squared error(MSE)between the predicted and measured results of health status is 2.11×10-5,the root mean squared error(RMSE)is 0.004 6,and the mean absolute error(MAE)is 0.003 1.This method can provide reliable support for the predictive maintenance of diesel engine air systems.
Keywords：diesel enginehealth status assessmenthealth status predictionLSTM network
Publication Date：2025-11-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：10( 12-20,33 )