Abstract
Modern non-road mobile machinery (NRMM), such as equipment used in agriculture, earthmoving, lifting and hoisting, construction, and container and bulk handling, is equipped with numerous sensors as standard. The sensor data required for the proper operation of these machines is typically communicated to their various subsystems via a CAN network. Since the introduction of the J1939 standard, the communication layer of the CAN protocol used in trucks and NRMM applications has been partially standardised. This makes it easier to retrieve sensor data from the network and use it, for example, to generate additional operational and strategic insights. Capturing and purposefully using all these sensor data represents an enormous opportunity for NRMM operators, but this potential remains underutilised today.
Furthermore, prior interaction with the target group has shown that unplanned machine downtime is a critical cost factor that directly puts pressure on profit margins in the sector. From an economic perspective, any tool that can reduce or prevent such downtime is therefore essential. Predictive maintenance (PdM) offers a solution by using advanced data analysis, including artificial intelligence and machine learning, to predict failures before they result in costly interruptions to operations. Although the academic literature has extensively demonstrated the effectiveness of sensor-data-based PdM, its adoption in Flemish industry remains limited.
The main barrier to adoption is not a lack of domain knowledge. Technical staff at the target companies are highly aware of the most common defects and their associated financial impact. However, in-depth knowledge, practical skills, and awareness of the enormous potential of AI for using J1939 data to proactively prevent these known issues remain insufficiently developed. There is an acute shortage of professionals capable of bridging the gap between in-depth machine knowledge and advanced data analysis. This is partly because NRMM operators are not yet actively seeking such profiles on the labour market. As long as this synergy is lacking among technical staff and operators, the technological opportunity will remain unrecognised, and demand for external expertise or internal innovation initiatives will fail to emerge. This project aims to bridge precisely this gap by translating the use of J1939 data and advanced AI capabilities into the familiar working practices of technical personnel.
In this project, Karel de Grote University of Applied Sciences and Arts builds on the expertise recently developed within its Centre of Expertise for Sustainable Industry in decoding and analysing CAN and J1939 data. This expertise was acquired through the RevCAN, U-CANsim, and CremAN projects, which successfully achieved brand-independent data capture and the simulation of vehicle and machine data for industrial applications.
The Industrial Vision Lab (InViLab) at the University of Antwerp builds on recent research in probabilistic machine learning and data-driven modelling. It will apply this expertise to translate J1939 sensor data from NRMM into robust predictive-maintenance applications.
In summary, the large volume of available sensor data offers a unique opportunity to further reduce NRMM downtime through predictive maintenance. However, fleet operators and maintenance providers currently lack sufficient awareness and expertise to effectively unlock and capitalise on this technological potential.
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