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TWI Hellas drives AI-based asset integrity management

Thu, 12 December, 2019

In today’s fast paced, technology and data rich environment, old approaches to assess performance management are falling short, which can lead to high operational, repair and maintenance costs as well as, ultimately, asset failure and associated downtime.

However, there is now a new approach to asset integrity management, based on artificial intelligence (AI), that is coming into the mainstream and can have significant benefits for owners and operators of plant, machinery and equipment.

TWI’s office in Athens, TWI Hellas, assists companies, particularly SMEs, with developing OEM-agnostic telematics, a technology-based solution which aggregates data from all machinery to provide insights, predictions and recommendations for improved asset management. The outputs can then be used for operational planning, understanding potential failure mechanisms and optimising maintenance strategies, ultimately leading to greater financial control and better risk management.

Dr Panagiotis Chatzakos, who manages TWI Hellas, explains “When we work with companies, we initiate an asset optimisation process which begins with ensuring the integrity of all their data, including cleansing, normalising and enhancing existing data sets. Then we develop a dedicated application, compatible with their existing systems, which will provide data-driven insights including cost analyses.”  “We also produce an action plan – using root-cause analysis, failure mode and effects analysis, and historical work order data – in order to determine why, where and how assets might fail, how critical the incident could be and what the overall risk of asset failure is” he continued.

In particular, TWI Hellas works closely with companies to guide the solution development process, de-risking the adoption of new technology by following a discovery-led planning route that incorporates four key steps:

  1. Specification of desired project outcomes based on clear objectives to inform performance measurement
  2. Identification of assumptions that must be proven for project outcomes to be realised, differentiating between what is within control and what cannot be accounted for
  3. Implementation of a project plan to determine feasibility of concept and whether critical assumptions are reasonable, to enable achievement of the desired result
  4. Strategy delivery in order to prove key assumptions of the project, followed by transference of lessons learnt to the wider organisation and future cascade of benefits to the business.

The outcomes include improved decision making due to predictive insights that inform proactive maintenance decisions, enabled by the application of industrial AI and machine learning engines to detect asset anomalies, and the leveraging of asset-centric visualisations to help operators understand how they can improve financial outcomes.

To find out how you can equip your company with data-based insights, and take the first step towards better prediction and prevention of issues before they occur, informed maintenance programmes and full optimisation of costs, simply email

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