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FAMOUS - Federated Anomaly Modelling and Orchestration for modUlar Systems

Reference number
Coordinator Scania CV AB
Funding from Vinnova SEK 8 922 297
Project duration November 2020 - December 2023
Status Ongoing
Venture Electronics, software and communication - FFI
Call Electronics, Software and Communication - FFI - June 2020

Purpose and goal

The goals of the FAMOUS project are: to develop a federated protocol and models for fault detection with intermittent connected vehicles that guarantees converegence of the federated models; to integrate the federated models with a hierarchical clustering based on the underlying modular system of Scania´s vehicles; and to develop a scalable and flexible vehicle edge analytics solution for efficient development, testing, and deployment of models as well as data streaming for selected time-series sensor signals.

Expected results and effects

The planned results are to develop federated anomaly detection methods optimized for edge computing and evaluated on injected faults and on a test fleet with methods that maps anomaly classes to known or undiscovered faults. The federated anomaly detection method will be developed for a diverse vehicle fleet. Finally, the project will build a scalable vehicle edge device prototype for orchestration of federated learning, model deployment and model testing.

Planned approach and implementation

The project will be executed with one full time post-doc researcher and two part time professors from LiU for two and a half years. Crosser will have the equivalent of 0.4 full-time development engineers for two and a half years and Scania’s resources will be equivalent to 3.28 full-time data scientists and development engineers for two and a half years. The project consists of five work packages from which Crosser and LiU will lead one each and Scania the remaining three.

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

Last updated 11 May 2022

Reference number 2020-02916

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