Determination of Multi-Component Failure in Automotive System using Deep Learning

Abstract

The connectivity of modern vehicles allows for the monitoring and analysis of a large amount of sensor data from vehicles during their normal operations. In recent years, there has been a growing interest in utilizing this data for the purposes of predictive maintenance. In this paper, a multi-label transfer learning approach is proposed using fourteen different pretrained classifier models retrained with engine simulation data to predict the failure conditions of a selected set of engine components. The retrained classifiers are designed such that the failure modes, including multimode failure, of an EGR, Compressor, Intercooler, and Fuel Injectors of a four-cylinder diesel engine can be identified. Time-series simulation data of various failure conditions, which includes performance degradation, is generated to retrain the classifier models to predict which components are failing at any given time. The test results of the retrained classifier models show that the overall classification performance is good, with the value of mean average precision varying from 0.7 to 0.75 for most retrained networks. To the best of the authors' knowledge, this work represents the first attempt to characterize such time-series data utilizing a multi-label deep learning approach.