Asset Performance Management for Power Plants

Reliability made simple

Have you ever asked yourself one of the following questions?

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How can I avoid costly downtime?


Knowing how equipment fails allows you to make the appropriate maintenance decisions at the right time.

Proactively avoiding equipment failure is what separates a profitable power plant apart from the rest.

How can I get the best performance out of my assets?


Truly understand how your assets function and come to fail.

Only then, you can push your assets to their limits while keeping asset health in check.

How can I eliminate unnecessary operations & maintenance work?


Firefighting can only get you through the day while more important problems are waiting to be solved.

Take a step back and with expert help take back control of your maintenance priorities to meet difficult production targets.



"The more information, the more data we have available on previous events, the better become our predictions."
Christopher Requinto, Reliability Manager at San Gabriel Power Station, Philippines

Find out how APM can help you in your power generation business






APM Model Library in a nutshell

APM is a tool-enabled transition to reliability-centered maintenance and risk-based inspections. Core of the system is the model library consisting of these elements:

Assets to be prioritized and selected based on criticality

Assets classified by criticality, considering aspects on

  • Health & safety
  • Environment
  • Availability
  • Reputation
  • Production
  • Economic impact

Failure Mode Effect Analysis (FMEA) 

In the Failure Mode Effect Analysis (FMEA), potential failures of the assets and associated effects as well as required (corrective) tasks will be described.


Failure Mode

Defines the event that causes an asset to fail to perform its function. For example, if a pump’s impeller becomes worn, the pump cannot convey liquid at the required rate.


Failure Effect

Defines the consequence of a failure mode. For example, when a pump’s impeller becomes worn (failure mode), the flow through the pump declines until it no longer delivers liquid at the required rate.


The Siemens Energy APM model library can help to effectively manage a variety of indicators that can be implemented tailored to your needs:

  • Online indicators: data taken from the DCS or other integrated data sources
  • Manual indicators: data taken e.g. from manual inspection rounds
  • Rule-based and/or calculated indicators: All rules can be combined with and/or for the rule-based indicators 

Health Index

The health Index aims at giving an indication on the overall condition of the assets.


Health Index (HI) overview:

  • The HI is determined by evaluating several factors that are critical to the operational health of the asset
  • Each critical factor is weighted during the HI calculation so that the relative value of each factor can be adjusted
  • Available information could be used as possible influence factors, e.g., filtered data, indicator readings or calculations, amount of alarms

Remaining Useful Life

The remaining Useful Life can indicate the remaining time that an asset may be able to function before required replacement.


  • The remaining useful life is an estimate of the amount of remaining time that an item, component, or system is estimated to be able to function in accordance with its intended purpose before economically required replacement
  • The remaining useful life is estimated based upon filtered data, indicator readings or calculations, amount of alarms, cost information, etc.

Technical Paper: Asset Performance Management 4.0: Predict with Confidence within the Digital Twin

This technical review focuses on asset performance management technologies for Industry 4.0 as applied directly to the Power Generation industry. As we move from digitalization strategy to digital transformation, we use the Digital Twin to gain prediction insights and to enhance day-to-day processes for better business outcomes. See the technology behind the story, to visualize and contextualize data exactly when and where needed for effective operational, maintenance and engineering decisions. Digital Twins play a vital role towards bridging the gap between CapEx and Opex to lower total cost of ownership and deliver optimal return on asset. 


Download technical paper

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