FEATURE : STORAGE
Boris Marrone , Vice
President , Asia Pacific and Head of South Asia and South East Asia , AVEVA
Rapid time to value
PETRONAS hired Trisystem Engineering Sdn Bhd ( TSE ), a systems integrator , to deliver the project . TSE worked closely with the team at PETRONAS to deploy the solution across various sites . In each case , the solution was up and running , delivering value within two months .
AVEVA Predictive Analytics comes with out-of-thebox purpose-built AI that has been customized for each industry , meaning that no coding is required . This enabled TSE to follow a templated approach that ensured the solution could be scaled quickly , deployed efficiently and deliver the high time to value that PETRONAS sought .
weeks or even months before failure . This helps assetintensive organizations , such as PETRONAS , to reduce equipment downtime , increase reliability , improve performance and safety , and reduce operational and maintenance expenditure .
At PETRONAS , the solution works in conjunction with the PI System from OSIsoft , now part of AVEVA , which gathers data from critical assets in the plant . The PI System collects and structures this data for historization and analysis . The data is used by AI-based AVEVA Predictive Analytics models to highlight any anomalies , trends , potential incidents or failures , and enable the teams to undertake improvements as needed .
Data collected by the sensors in the instrumentation and equipment pinpoint the tiniest deviations in what the AI has trained the software to consider as ‘ known good behavior .’ This is more effective than setting high and low thresholds that trigger an alarm when reached , because , by then , operations have already spun out of control . PETRONAS ’ AVEVA Predictive Analytics solution spots the problem as it grows away from ‘ known good behavior ’ before it leads to a catastrophic failure .
Mohd Nazrin Zaini , Senior Engineer ( Rotating Equipment ), PETRONAS , said : “ Our Digital Transformation strategy at PETRONAS is to add value quickly , as this has a faster impact on our sustainability goals and on profitability . We do this by identifying discrete projects with tangible deliverables and then cascade the same approach elsewhere , having learned valuable lessons along the way . AVEVA Predictive Analytics allowed our teams to adopt a templated approach that enabled us to quickly deploy the solution in other sites , ensuring high time to value and fast ROI .”
New ways of working
The maintenance and reliability engineers at PETRONAS use AVEVA Predictive Analytics for their day-to-day tasks , to monitor assets across the sites . All levels have visibility of the systems , from technicians to plant managers as well as management teams .
With these capabilities in the cloud , PETRONAS can remove silos and build new and more collaborative ways of working . In elevating digital fluency to its people , PETRONAS expects these successful pilot programs to spread the word in their digitalization journey . The approach is not about convincing employees , but rather immersing them in new ways of working through digital solutions .
Salim Sumormo , Custodian ( Rotating Equipment ), PETRONAS , said : “ We ’ ve been using the PI System as our standardized data historian platform for many years . We were looking to add further value to the data gathered to optimize plant operations throughout our business . We chose cloud-based AVEVA Predictive Analytics not only because of its ability to accurately predict equipment failures in advance , but also because it easily integrates with the PI System and because of its intuitive look and feel which helped our teams get up to speed quickly .”
Predictive analytics drives business value , saving US $ 17.4M ( RM73.1M )
The pilot implementation accurately predicted failures in advance that enabled PETRONAS ’ team to fix issues ahead of actual failures . In 2020 ( the first year of deployment ) with 200 models deployed , the solution accurately identified 51 major early warnings , creating a value of RM73.1M , equivalent to savings of US $ 17.4M , and 14x ROI . Out of the 51 warnings , 12 were identified as high-impact warnings . Resolving these ahead of
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