Research Strands

The research strands focus on specific issues identified after extensive consultation with industry. There is one core strand, which will underpin research in the elective strands. The elective strands are designed to push the frontiers of Integrated Vehicle Health Management (IVHM) solutions.

Managing the evolution of a vehicle as it passes through design and manufacturing and then through many maintenance, repair and component upgrade cycles is critical for safety, fitness for performance and legislative compliance. The industry faces a number of challenges today in effective capture and management of data, specifically regarding the usage and maintenance of a vehicle through its life. This strand aims to develop a vehicle life cycle information management model which will map all existing lifecycle processes for vehicles and their components into a single unified framework. 

Elective Strand 1: Effective validation of service processes

This aims to design effective service strategies and standard service procedures, supported by automated data capture technologies that will ensure service operations are performed correctly, and recorded effectively. This will exploit information provided by IVHM methodologies to ensure a high rate of "right first time" in repair operations.

Elective Strand 2: Service prediction and scheduling

Due to increasing customer pressure to maximise vehicle availability, manufacturers have to examine innovative methods to monitor the condition of vehicles and ensure availability of resources and spare-parts in the event of equipment failure or predictive/preventive maintenance. For effective management of service provision, knowledge regarding usage as well as the service history of the vehicle is essential.
This strand aims to develop strategies, algorithms, and prototypes for automated service prediction and scheduling systems aimed at reducing maintenance costs and vehicle downtimes.  The new algorithms will exploit information regarding the residual life of components embedded within vehicles in the field as calculated by IVHM methodologies. This will help in predicting failure of critical components in addition to improved scheduling of resources and maintenance routines.

Detection and prevention of counterfeit parts in the supply chain is critically dependent on the ability to trace the full history of parts in a timely, accurate and complete manner.This strand aims to develop a track and trace model specific to vehicle industry requirements and tools aimed at detection and prevention of counterfeit components, thus enhancing the ability to monitor and control product quality throughout the supply chain.

The output of this strand could also be extended to develop methodologies for forward as well as reverse tracking and tracing within the supply chain to identify for example, sources of defects. This in turn enables manufacturers to have a more focused recall in the event of defects that have arisen at production, assembly, or transportation. We will demonstrate this in laboratory systems and will support pilot studies at sponsor organisations.

This aims at developing tools that combine data about vehicle condition with location information to enable (i) ensuring availability of spare parts at appropriate locations; and (ii) dynamic routing of spare parts between locations.