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Optimising asset performance using remote technologies.

Dr Neil Arthur Our expert voice on asset maintenance optimisation. View profile

A reliable plant is a safe plant and a cost-effective plant.

An inability to fully address the causes of unplanned downtime, an overwhelming number of work orders, current movement restrictions and remote working, all impact on operational performance and risk. LRQA's Dr Neil Arthur walks you through how a dynamic approach to asset management that uses remote technologies and tools can optimise maintenance and performance.

When it comes to industrial assets, performance and risk go hand in hand. Business leaders need to be confident that threats to productivity, safety and the environment have been mitigated. Yet recent well-documented events, such a fire at a Philadelphia refinery which injured 37 people, or the ruptured gas line in Kentucky which proved fatal, are evidence that more needs to be done to avoid future catastrophic events.

Couple this with ever increasing scrutiny of performance and risk metrics, and having a comprehensive view of asset performance and the risk of failure takes on even greater importance and this can be achieved remotely.

Asset owners/operators are seeking solutions and technologies that provide accurate, up-to-the-minute data for reporting and decision making. They want assurance that any asset performance risk and repercussions have been identified and addressed.

When it comes to convincing management of the value of managing assets, nothing works better than demonstrated returns and taking a more focused approach to performance management has clear rewards. For instance, one supermajor reduced OPEX by $18million and cut 74,000 hours of offshore labour in 12 weeks. Another offshore facility operator was able to increase safety while achieving a 30% reduction in planned maintenance.

Predictive analytics

Predictive analytics and risk modelling capabilities will take asset performance and maintenance to new levels, even more so with the use of artificial intelligence and machine learning.

The recent advances in big data, IIoT machine connectivity and cloud technology have created new opportunities to get actionable data from all types of industrial/plant assets, remotely – anytime, anywhere. Currently, organisations in the heavy process industry are collecting vast amounts of structured and unstructured data, but are often incapable of using it, at least not to its full potential. However, AI-powered systems could have the ability to draw on this previously unmanageable amount of data to deliver unprecedented insights into the true state of assets.

Quantitative maintenance optimisation

Quantitative maintenance optimisation allows modulation of maintenance activity, effort and cost in response to changing reliability, economic and safety factors.

In this way, a ‘live’ model of the facility is developed and planned maintenance activity can be scaled in response to changes in the technical and commercial environment. It is LRQA's experience that a net 30% reduction in planned maintenance can be delivered through these techniques when compared with conventional approaches.

Digital twins

Digital twins may have the potential to transform the future performance of assets by using new risk and reliability methodologies, failure data libraries, modelling tools and advanced analytics to process vast amounts of inspection and maintenance data.

Modern facilities are obtaining actionable insights that identify risk and enhance asset and plant performance and reliability. With cloud accessibility and better processes to capture data, predictive and prescriptive analytics could mitigate risk in complex industries by helping people understand what could happen, and when.

Maintenance analytics and benchmarking

Advanced analytics is another innovative use of data, which could enable automated interrogation of historic maintenance and inspection information, helping to derive forward-looking equipment reliability data and asset performance.

Typically performed manually or semi-automatically, a new, automated approach will allow large-scale maintenance optimisation and performance benchmarking at pace.

Integrating with Enterprise Asset Management (EAM) systems

Harnessing the power of these technologies can be a daunting task and an efficiently operating Enterprise Asset Management (EAM) system is critical.

Steps to increase the ROI of your EAM include:

  1. Taking the time needed upfront to get design and implementation correct – this applies whether you’re looking at your first EAM system, or re-structuring your existing set-up.
  2. Using a multi-disciplinary team to design and implement the EAM, to correctly reflect how the system will be used in operations now, and in the future.
  3. Specifying and optimising KPIs, so operational teams and managers can focus on workflow, corrective maintenance scheduling, work order prioritisation and backlog management.

It is also important that with a properly functioning EAM system in place, that you leverage the value in your data. Terabytes of valuable information are often stored in these systems and full advantage must be taken of the insights and efficiencies on offer, to help inform optimisation decisions.

Setting up an EAM in an intelligent way can pay huge dividends and minimise OPEX. Combining this with a fully-functioning asset performance management solution will undoubtedly unlock enhanced analytical capabilities through a single platform for managing reliability and risk.

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