A reliability inspired strategy for intelligent performance management with predictive driver behaviour: A case study for a diesel particulate filter

YesThe 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

Impact of duty cycle on wear progression in variable-displacement vane oil pumps

YesVariable-displacement vane oil pumps are increasingly employed in automotive powertrains for their efficiency benefits through reduced losses. However, confirming long life reliability of a new commodity based on limited data available from product development tests and early field experience is a significant challenge, which is addressed by the work presented in this paper. The approach presented combines physical examination of pumps returned from tests, with analysis of damage factors for pump wear progression, and an analysis of functional parameters for the powertrain system focused on the cause effect linkages across the systems hierarchy. The metrology results from physical measurements of used parts provide useful insights for the wear progression and the expected service performance of the pump. The paper also expands towards a data driven approach based on ECU data analysis that could provide a pathway towards the development of online health monitoring and diagnostics of the oil pumps.Research project: “Intelligent Personalised Powertrain Health Care”, funded by Jaguar Land Rover

Automotive IVHM: Towards Intelligent Personalised Systems Healthcare

YesUnderpinned by a contemporary view of automotive systems as cyber-physical systems, characterised by progressively open architectures increasingly defined by their interaction with the users and the smart environment, this paper provides a critical and up-to-date review of automotive Integrated Vehicle Health Management (IVHM) systems. The paper discusses the challenges with prognostics and intelligent health management of automotive systems, and proposes a high-level framework, referred to as the Automotive Healthcare Analytic Factory, to systematically collect and process heterogeneous data from across the product lifecycle, towards actionable insight for personalised healthcare of systems.Jaguar Land Rover funded research “Intelligent Personalised Powertrain Healthcare” 2016-201

Towards a Model-Based Systems Engineering Approach for Robotic Manufacturing Process Modelling with Automatic FMEA Generation

YesThe process of generating FMEA following document-centric approach is tedious and susceptible to human error. This paper presents preliminary methodology for robotic manufacturing process modelling in MBSE environment with a scope of automating multiple steps of the modelling process using ontology. This is followed by the reasoning towards automatic generation of process FMEA from the MBSE model. The proposed methodology allows to establish robust and self-synchronising links between process-relevant information, reduce the likelihood of human error, and scale down time expenses

Automotive IVHM: a framework for intelligent health management of powertrain systems. Development of a framework and methodology based on the fusion of knowledge-based and data-driven modelling approaches for diagnostics and prognostics of complex systems with application to automotive powertrain systems

The full text will be available at the the end of the embargo period: 29th Jul 202

Driving Behaviour Modelling Framework for Intelligent Powertrain Health Management

YesImplementation of an intelligent powertrain health management relies on robust prognostics modelling. However, prognostics capability is often limited due to unknown future operating conditions, which varies with duty cycles and individual driver behaviours. On the other hand, the growing availability of data pertaining to vehicle usage allows advanced modelling of usage patterns and driver behavious, bringing optimisation opportunities for powertrain operation and health management. This paper introduces a methodology for driving behaviour modelling, underpinned by Machine Learning classification algorithms, generating model-based predictive insight for intelligent powertrain health management strategies. Specifically, the aim is to learn the patterns of driving behaviour and predict characteristics for the short-term future operating conditions as a basis for enhanced control strategies to optimise energy efficiency and system reliability. A case study of an automotive emissions aftertreatment system is used to comprehensively demonstrate the proposed framework. The case study illustrates the approach for integrating predictive insight from machine learning deployed on real world trip behaviour data, in conjunction with a reliability-based model of the operational behaviour of a particulate filter, to propose an intelligent active regeneration control strategy for improved efficiency and reliability performance. The effectiveness of the proposed strategy was demonstrated on an industry standard model-in-the-loop set up with a representative sample of real-world vehicle driving data.The authors acknowledge funding for the research presented in this article from Jaguar Land Rover under a research collaboration with the University of Bradford on “Intelligent Personalised Powertrain Healthcare”, and the Institute of Digital Engineering who have provided funding for proof of concept – the aiR-FORCE project