VEMUT-Sub-Sampling: A Novel Method for Sparse Multivariate Time-Series Vehicle Data

In today's automotive industry, modern vehicles are equipped with numerous sensors to monitor the vehicle's state. Accurate prediction of component wear-and-tear requires the identification and alignment of the appropriate sensors. However, this task is complicated by the sheer number of sensor features, their intermittent logging activity, and non-uniform data. Furthermore, the number of vehicles deemed suitable for analysis is exceedingly limited. In this paper, we address the prevalent challenge of sparse, irregular, and multivariate time series data encountered in various domains, with a particular focus on the automotive industry. Conventional methods like imputation or interpolation, which attempt to estimate data points, are inapplicable when the dataset is limited in size, consists of highly irregular time spans, a large feature space, and data sparsity. To tackle these issues, we introduce a novel subsampling methodology for VEhicle MUltivariate Irregular Time series data (VEMUT). Utilizing VEMUT, multiple sub-samples generate an increased dataset size, allowing machine learning models to learn high-performing and generalizable models, even when the dataset is limited in size. We examine the performance of the resulting dataset from VEMUT's sub-sampling across three simple network architectures: a fully convolutional network, a fully convolutional network with attention, and a convolutional network with pooling. Our findings reveal over 99% accuracy for all architectures on the validation data, leading to a correct prediction of the wear-and-tear of a Porsche Taycan's air suspension module. These results highlight the potential of our approach to significantly enhance models capable of recognizing patterns in data and predicting future developments. Such advancements are especially vital for prognosing wear-and-tear of various components in modern vehicles, ultimately contributing to improved maintenance and cost efficiency.