HEALTH
Hazard Estimation and Analysis of Lifelong Truck Histories
| HEALTH | |
| Project start: | |
|---|---|
| 9 October 2017 | |
| Project end: | |
| 31 October 2019 | |
| More info (PDF): | |
| Contact: | |
| Slawomir Nowaczyk | |
| Application Area: | |
| Intelligent Vehicles | |
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Abstract
FFI Vinnova project
Purpose and goals
Unplanned downtime can be avoided by accurate prediction of the failure through continuously monitoring of vehicles’ health status. However, to reveal patterns behind failures in a system as complex as a modern truck, new methods for analysing the data need to be developed. The HEALTH project aims to create a sequence model capturing the lifelong history of a truck, and use it to explain relations between different events such as failures, repairs, fault codes - leading to better maintenance.
Expected effects and results
HEALTH will enhance current 100% uptime promise of Volvo Trucks by expanding the existing range of predictive maintenance solutions. Novel Machine Learning methods for representing lifelong histories of trucks will be used to precisely identify vehicles that are likely to fail soon, and corrective actions will be suggested based on the probable failure causes. Overall effects will include prolonging vehicle life, providing more timely and cheaper maintenance, and increasing traffic safety.
Scheduled planning and implementation
The HEALTH project is planned for two years, starting October 2017. The work will be carried out in a close collaboration between Volvo Trucks Aftermarket and Halmstad University. The project is divided into five work packages focusing on data aggregation, fully and partially observable sequence modeling, causal analysis and the demonstrator. Implementation includes research and development of new machine learning methods, their deployment, and finally evaluation in real business setting.
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