Maximizing Asset Health Index Value and Fidelity with APM Health Software

The asset maintenance journey of an energy organization is typically seen as a progressive transformation from reactive to prescriptive strategies, driven by the criticality of equipment, digital technologies, the need for operational excellence, and profitability.

Traditionally, operators in the energy sector have relied heavily on reactive maintenance, addressing equipment failures only after they occur. This outdated and typically manual approach often led to unplanned downtime, high repair costs, and safety risks.

To mitigate the unpredictability of reactive maintenance, energy organizations have slowly transitioned to time-based maintenance strategies. Time-based maintenance involves periodic maintenance at scheduled intervals, regardless of the equipment's actual condition. While time-based inspections reduced unexpected failures, in some cases this approach would still entail high operational costs due to replacing parts that still had useful life remaining. This could also have a negative impact on sustainability initiatives – increasing waste and resource depletion.

As operators started to embrace advancements in sensor technology, the influx of new datasets led to a gradual shift of condition-based maintenance. By continuously monitoring asset parameters (pressure, temperature, vibration, etc.) energy organizations could now assess the actual health of their assets in near real-time. Condition-based maintenance allowed for maintenance activities to be performed based on equipment condition, leading to more efficient use of resources. The approach reduces downtime and improves productivity.

Building upon condition-based strategies, predictive maintenance emerged as a more advanced approach – specifically for high-value assets that impact production. This approach lets operators use historical data and machine learning algorithms to forecast potential failures before they occur. Proactive strategies enabled maintenance teams to address issues weeks or months prior to breakdown and subsequently extend equipment life.

Finally, the culmination of this journey is prescriptive maintenance, which not only predicts potential failures but also recommends specific actions to prevent them. Utilizing advanced analytics, artificial intelligence, and machine learning, prescriptive maintenance provides actionable insights tailored to individual assets in the facility.

Energy organizations at varying levels of digital maturity faced with growing industry challenges (capacity constraints, navigating the workforce, economic dynamics, regularity demands, etc.) have a decision to make: What is the ideal starting point for their asset maintenance journey?

An asset health index is a foundational element that provides a single pane of glass view where users can make the best maintenance decisions based on risk, cost, and availability – or as we like to call the Asset Performance Management (APM) trifecta.

APM Health, part of GE Vernova’s comprehensive APM software suite, allows users to transform data into tangible maintenance knowledge. This includes data from sensors, equipment, OT/IT systems, timeseries, historians, rounds, alerts, and more. Furthermore, data can be contextualized with the objective of providing users a holistic asset health maintenance assessment (AHMA) that encompasses a series of key performance indicators that allows energy organizations to prioritize what needs to be done in order to increase availability and asset reliability while reducing cost and overhead. A key feature of the asset health maintenance assessment is the asset health index (AHI) – a numeric calculation that quantifies the asset's overall condition. This allows users to direct attention to the lowest performing assets, estimate remaining life, and build a targeted maintenance strategy based on need – at a site, fleet or enterprise level as the case may be.

The APM Health application also has several modules that work in conjunction to enhance the fidelity of this asset health index. This includes:

Yet, asset health is only a starting point that is fundamental to the maintenance journey. GE Vernova’s APM Health works with other applications and tools in the overall APM suite to improve overall customer experience and reduce operational risk. For instance, utilizing APM Connect, users can send recommendations directly to their EAM system for work order execution. APM Health can also integrate with the APM Strategy application, a solution that optimally builds asset strategies by balancing criticality, risk, and cost. Here, users can view known equipment risks and the health indicators assigned to mitigate them. Additionally, using Policy Designer, new health indicators can be created and customized based on user requirements. And finally, if users have high-value assets, they can utilize GE Vernova’s SmartSignal offering to predict failures before they happen – evolving to a more predictive and prescriptive approach to maintenance.

The energy sector is truly in the digital age, and an enormous amount of data available to operators for decision making. Specifically for maintenance optimization, the contextualization of asset condition supports reliability and longevity of equipment, improvement of service delivery, and reduction of operational risks. Every bit of asset data contains potential insights to be unlocked, and GE Vernova’s APM Health application is the right solution for you to start your maintenance journey. [Watch our Interactive APM Health Demo]

The ability to consolidate data from various sources, process it, and produce a health score/index allows users to make decisions on where the criticality resides – where they would see highest impact from O&M actions. Furthermore, as energy organization look to scale operations, users can also opt to combine condition-based maintenance with time-based maintenance and OEM recommendations or even adopt proactive maintenance strategies for their high value assets using GE Vernova’s holistic APM solution.

Contact us to learn more.

Additional Resources: Customer Value Stories