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RAPIDS - Reliable Adaptive Predictive maintenance and Intelligent Decision Support

Reference number
Coordinator Scania CV AB
Funding from Vinnova SEK 9 698 574
Project duration January 2022 - December 2024
Status Ongoing
Venture Electronics, software and communication - FFI
Call Electronics, software and communication - FFI - June 2021

Purpose and goal

The project revolves around developing machine learning models based on increased availability of streamed log data from vehicles and integration of these models in the decision-making processes for maintenance. It specifically deals with how uncertainty in predictions can be estimated and weighed in order to make robust individual-based decisions. Central is also how new data can be fed back to the models in order to improve performance and predictive power over time.

Expected results and effects

The project will help to strengthen the research fields of machine learning and forecasting. The project contributes with findings for how uncertainty in predictions should be estimated and weighed in order to make robust and individual-based decisions and how new information can be fed back to prediction models to improve performance and predictability over time. Methodology for how to optimally use streaming monitoring signals for maintenance planning contributes to better interaction between forecast models on different time scales and on board and in the cloud.

Planned approach and implementation

The project develops theory and generally applicable methods which are then tested and demonstrated on real and interesting user cases. The intention is to have an iterative and incremental work process. The project consists of 5 work packages (WP). WP 1 performs administration and management of the project. WP 2 and 3 focus on predictive models for forecasting based on streamed data and on how uncertainty information can be quantified and weighed in for decisions. WP 4 and 5 work with efficient handling and feedback of data.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 19 January 2022

Reference number 2021-02522

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