Transformer-based Analysis of Vehicle CAN Bus Data for Predictive Maintenance: A Real Case Study

The use of machine learning techniques to analyze Controller Area Network (CAN) bus data transmitted by fleets of industrial vehicles has been increasingly explored. The main industrial applications include avoiding service disruptions and vehicle damage, improving operational efficiency, and reducing cybersecurity risks. However, the application of predictive maintenance techniques to industrial vehicle data is challenged by the high-dimensional and heterogeneous nature of the signals, their variable quality, and the limited availability of public benchmarks and human annotations. In this work, we describe a real-world industrial case study based on company data acquired from fleets of thousands of commercial vehicles over several years. We design a machine learning pipeline to early detect vehicle faults based on the analysis of CAN bus signals and evaluate the performance of several prediction models, including a newly proposed transformer-based architecture. Furthermore, we also demonstrate that existing public benchmarks fail to capture the complexity of real industrial scenarios, highlighting the need for more realistic and comprehensive analysis and benchmarks.