Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component Aging

Abstract

Predictive maintenance has become important for avoiding unplanned downtime of modern vehicles. With increasing functionality the exchanged data between Electronic Control Units (ECU) grows simultaneously rapidly. A large number of in-vehicle signals are provided for monitoring an aging process. Various components of a vehicle age due to their usage. This component aging is only visible in a certain number of in-vehicle signals. In this work, we present a signal selection method for in-vehicle signals in order to determine relevant signals to monitor and predict powertrain component aging of vehicles. Our application considers the aging of powertrain components with respect to clogging of structural components. We measure the component aging process in certain time intervals. Owing to this, unevenly spaced time series data is preprocessed to generate comparable in-vehicle data. First, we aggregate the data in certain intervals. Thus, the dynamic in-vehicle database is reduced which enables us to analyze the signals more efficiently. Secondly, we implement machine learning algorithms to generate a digital model of the measured aging process. With the help of Local Interpretable Model-Agnostic Explanations (LIME) the model gets interpretable. This allows us to extract the most relevant signals and to reduce the amount of processed data. Our results show that a certain number of in-vehicle signals are sufficient for predicting the aging process of the considered structural component. Consequently, our approach allows to reduce data transmission of in-vehicle signals with the goal of predictive maintenance.

Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering

“…Therefore, without real-time capability, it is still possible to build digital twins based on aggregate fleet data. Correspondingly, the bi-directional connectivity could be enabled by vehicular telemetries and over-the-air updates, which are the state-of-the-art in the automotive industry [14], [17].…”

“…With the rapid development of mobile communications technology, sensor data, such as on-board signals, are expected to be logged. For example, Sass et al [14] demonstrate the potential for predicting component aging with logged signals. However, Wilberg et al [15] highlight that performing massive data logging for customer fleets may result in privacy violations.…”