Difference between revisions of "VBPM"

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|ContactInformation=Sławomir Nowaczyk, Yuantao Fan
|ContactInformation=Sławomir Nowaczyk, Yuantao Fan
|ShortDescription=Volvo Bus Predictive Maintenance
|ShortDescription=Volvo Bus Predictive Maintenance
|Description=The aim of Active@Work is to explore if mobile technology including a personalized decision support system, can have any effect on physical activity level, health, work ability, quality of life, work productivity or sick leave among individuals with osteoarthritis (OA). We also aim to study if there is any difference in effect between using mobile technology and activity monitoring alone or when continuous feedback concerning physical activity is added.
|ProjectResponsible=Sławomir Nowaczyk
|ProjectResponsible=Sławomir Nowaczyk

Revision as of 16:40, 9 November 2017

Volvo Bus Predictive Maintenance

Project start:
January 2017
Project end:
June 2018
More info (PDF):
[[media: | pdf]]
Sławomir Nowaczyk, Yuantao Fan
Application Area:
Intelligent Vehicles

Involved internal personnel
Involved external personnel
Involved partners


The overall objective of this project is to improve uptime for Volvo buses as well as scheduling maintenance cost-effectively. Guaranteeing vehicle uptime is important since downtime caused by component failures are increasingly difficult to identify and being dealt with as the complexity of modern transport solution increases. The project is aiming at developing a framework, powered by machine learning technique, for predicting component failures in buses and providing fleet operator decision support for scheduling maintenance. Proposed machine learning models will be built, tested and validated based on real data. This project is a collaboration with Volvo Bussar AB and Volvo Truck Technology.

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.