VBPM
Volvo Bus Predictive Maintenance
| VBPM | |
| Project start: | |
|---|---|
| January 2017 | |
| Project end: | |
| June 2018 | |
| More info (PDF): | |
| [[media: | pdf]] | |
| Contact: | |
| Sławomir Nowaczyk, Yuantao Fan | |
| Application Area: | |
| Intelligent Vehicles | |
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Abstract
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.
Background and Objectives
The current paradigm for maintaining industrial equipments is a combination of reactive and preventive actions. Take commercial transportation vehicles as example, they are typically maintained after an equipment failure occurs or according to preplanned visits to the workshops based on mileage or calendar time. This mixture of maintenance strategy is not ideal: i ) it does not perform maintenance pro-actively well before the failure happens, i.e. severe component failures usually result in extra damage to the system and could be prevented; ii ) planned maintenance with fixed time intervals does not guarantee all routinely changed parts have used all their potentials. Therefore, a shift of current maintenance strategy to one with more predictive maintenance is required: to inspect and repair components (well) before they causes a breakdown or severe damage to the system.
Nowadays, with the development of electronic devices and the emergence of Internet of Things, huge amount of sensor data collected and transmitted remotely can be utilized for equipment monitoring, fault detection and prognostics. By processing sensor date during operations, condition of the equipment will be accessed and maintenance decision will be made. This project will improve the work carried out in
This project will utilise data from Volvo bus, including Logged Vehicle Data and Vehicle Service Records.
Expectations
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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