Multi-Party DT-enabled Maintenance Ecosystem

Evolving from a single pane of glass view of entire asset lifecycle to dynamic maintenance and management of critical national infrastructure.

Use Cases 1 Feb 2020 by Ali Nicholl

Part of Rolls-Royce, one of the world’s leading industrial technology companies, Rolls-Royce Power Systems (RRPS) is a leading provider and manufacturer of high-speed and medium-speed reciprocating engines, complete propulsion systems and distributed energy solutions. 

RRPS commissioned IOTICS in 2019 to create a digital twin of its power generating unit (GU) for the internal use case of improving planned and unplanned maintenance activities. This was the first deployment in pursuit of RRPS’ vision of using digital twins to develop additional data-driven services that would benefit its customers and its customers’ customers – “Customer Service 4.0”.

RRPS’ Customer Service Manager wanted to solve customers’ issues quickly. However, finding information about a single asset across 150+ internal systems was challenging. They required a single access point to truth at an asset-level, showing the GU’s entire lifecycle interoperating across the relevant systems, databases, cloud platforms and operational technologies. Additionally, having access to all the relevant information about the GU through the digital twin supports the work of the RRPS Maintenance Engineer trying to keep their client’s fleet of 631 trains in optimum health. This included contextual information about the weather and levels of air quality (notably pollen) it had experienced during the day. 

IOTICS created virtual composites of the entirety of an asset’s data and controls: writing lightweight data modelling integrators into 8 pre-existing systems and then securely brokering access through IOTICSpace. Modelling the data using semantic web technologies enabled programmatic find and bind of asset-level information regardless of its systems of origin (such as SAP, Talend, GPS trackers, CSV files, bespoke internal systems). Data was then visualised in RRPS’ existing Customers Service application ‘Go!Manage’.

Within Go!Manage, historical asset information, MES, PDM, PLM, MRP, CAPP, and Service data artefacts can be interrelated with contextual customer and environmental information and presented for the Customer Service Manager’s review.

Once the internal use case was delivered, additional external use cases for the digital twins were rapidly identified. The location of the GUs was used to identify the location of the trains, monitor any directions from scheduled operations and enable interventions to ensure efficiency in the overnight maintenance of trains. The twins IOTICS created provide a dynamic schedule for every overnight location showing which trains are arriving when, if they are early, late, planned or reallocated. Maintenance planners have warnings about which maintenance plans to prepare and can increase time spent on value-add maintenance activities.


Additional benefits to the train manufacturer HITACHI (RRPS’ client) and the train operating company (HITACHI’s client) include understanding the spread of the fleet. This can help mitigate the risk of trains getting locked into certain patterns, which make longer-term maintenance activities difficult to plan and execute. 

The ecosystem is powered by digital twins of different assets interacting together. Our distributed architecture means that RRPS’ IOTICSpace has digital twins of GUs, HITACHI’s IOTICSpace has digital twins of the trains and depots, Network Rail’s IOTICSpace has digital twins of scheduling and points, and so on. Next steps are to continue to enrich the twins with more information, improving their intelligence and interactions. Proposed developments include: enriching the digital twin of the train, with health information from the onboard sensors, and the digital twin of the Depot, with stock and resourcing levels, to manage a train’s maintenance requirements more dynamically; use the digital twin of the train to share information about infrastructure health.


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