A RELIABILITY INSPIRED STRATEGY FOR INTELLIGENT PERFORMANCE MANAGEMENT WITH PREDICTIVE DRIVER BEHAVIOUR: A CASE STUDY FOR A DIESEL PARTICULATE FILTER

The increase availability of operational data from the fleets of cars in the field offers opportunities to deploy machine learning to identify patterns of driver behaviour. This provides contextual intelligence insight that can be used to design strategies for online optimisation of the vehicle perf...

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Bibliographic Details
Main Authors: Doikin, Aleksandr, Campean, Felician, Priest, Martin, Lin, Chunxing, Angiolini, Emanuele
Format: Conference Proceeding
Language:English
Subjects:
Aerospace engineering
Automobiles
Case studies
Design engineering
Gas flow
International conferences
Machine learning
Performance management
Preventive maintenance
Research methodology
Sensors
Useful life
Vehicles
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Summary:The increase availability of operational data from the fleets of cars in the field offers opportunities to deploy machine learning to identify patterns of driver behaviour. This provides contextual intelligence insight that can be used to design strategies for online optimisation of the vehicle performance, including compliance with stringent legislation. This paper illustrates this approach with a case study for a Diesel Particulate Filter, where machine learning deployed to real world automotive data is used in conjunction with a reliability inspired performance modelling paradigm to design a strategy to enhance operational performance based on predictive driver behaviour. The model-in-the-loop simulation of the proposed strategy on a fleet of vehicles showed significant improvement compared to the base strategy, demonstrating the value of the approach.
ISSN:2732-527X
2732-527X
DOI:10.1017/pds.2021.20