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 14 different pretrained convolutional neural networks retrained with engine simulation data to predict the failure conditions of a selected set of engine components. The retrained classifier networks are designed such that concurrent failure modes of an exhaust gas recirculation, compressor, intercooler, and fuel injectors of a four-cylinder diesel engine can be identified. Time-series simulation data of various failure conditions, which include performance degradation, are generated to retrain the classifier networks to predict which components are failing at any given time. The test results of the retrained classifier networks show that the overall classification performance is good, with the normalized value of mean average precision varying from 0.6 to 0.65 for most of the 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.