For a reliability engineer or maintenance manager concerned with the strategic management of equipment performance, it’s necessary to identify the equipment that is being over maintained and under maintained with respect to proactive maintenance. Proactive maintenance includes everything from preventative work such as changing the belts on a conveyor at a regular interval to condition monitoring activities, such as vibration analysis performed at a regular frequency. In instruments such as pressure transmitters, the proactive work of verifying the transmitter’s calibration might cause instrument errors if done too frequently and might cause drift induced errors if not corrected frequently enough. The errors due to too frequent proactive work are also known as maintenance induced errors. These maintenance induced errors are applicable to other types of equipment, such as steam turbines, reciprocating compressors and centrifugal pumps. By adopting a comparative analytic methodology, today’s data analysts can quickly identify over- and under-maintained equipment which helps industrial organizations increase asset reliability and even see improvements to their bottom line.

Determine the most effective strategy for reducing reactive work

To identify equipment in the over- and under-maintained categories, Figure 1 depicts a very simple yet effective tool. By plotting the emergency or reactive work count versus the preventative maintenance (PM) / predictive maintenance (PdM) count for each asset belonging to a specific type, such as steam turbines, analysts can determine the most effective strategy for reducing reactive work.

Graph showing reactive work count versus the PM and PdM count for each equipment.
Graph showing reactive work count versus the PM and PdM
count for each equipment.

The equipment shown in red has 100 PM/ PdMs in the two year period, but still experiences nine reactive or emergency work orders. This asset seems to have excessive proactive maintenance which is not effective in reducing reactive work orders. Whereas, the equipment shown in yellow has no PMs or PdMs and has eight reactive work orders. This asset seems to be under-maintained and needs more proactive maintenance to reduce reactive work.

In addition to identifying obvious equipment as in the graph shown in Figure 1, analysts must create zones and understand the groups of equipment that fall into each category. For this process, analysts need to identify the performance of similar equipment across different companies and different manufacturing sites. With the emergence of big data and advanced software tools, new platforms have the ability to provide the peer values for reactive work and PM/ PdM count and determine the zones and groups of equipment falling in each category.

By analyzing the peer data in the case above, analysts were able to determine that the peer PM/PdM count for all steam turbines was six and reactive work count was one. This sets a standard for how equipment should be performing. Using the peer values, reliability engineers split Figure 2 into quadrants and zones were created to categorize equipment.

Graph with peer values and zones for three equipment groups
Graph with peer values and zones for three equipment groups
determinedby plotting reactive work versus PM/PdM count.


Assets falling in the blue category have greater than average PM/ PdM count and less than average reactive count. These assets need to be evaluated for making the PM coverage lean and eliminating unnecessary tasks while maintaining the low reactive work. Assets falling in the red category, on the other hand, have greater than average PM/ PdM count and greater than average repair count. These assets are candidates for a PM/ PdM optimization program using a comparative analytics and an asset performance management (APM) solution to improve the quality and efficiency of the PMs and reduce reactive repairs. Further, assets falling in the yellow category have less than average PM/ PdM count and more than average reactive count. These assets need to be evaluated for increased PM/ PdM coverage to reduce reactive repairs.

These comparative analytics platforms can provide a plot of the proactive work count percentage (Proactive work count % is the percent of all proactive work orders (Pos and PdMs of the total work order count) versus the PM effectiveness percentage (PM effectiveness % = # of repairs detected proactively/total repair count). This helps in identifying equipment with similar proactive counts but a different PM effectiveness percentage. For example, the equipment denoted in red in Figure 3 has a proactive work count of 80% but an effectiveness of only 10%. Whereas, the equipment denoted in green has the same proactive work count of 80% but an effectiveness of 75%. In other words, the quality of PMs on the green equipment is much higher and much more effective in detecting potential problems before complete equipment failure.

Data on Steam Turbines
Assessing the effectiveness of proactive work by plotting proactive work
count % versus PM effectiveness %.

Identifying and address failure modes

After identifying these equipment groups, the next step should be to determine the dominant failure modes experienced on the equipment type and create PM and PdM tasks to address and check for these failure modes. This will greatly help in reducing reactive work orders as the PM and PdM tasks will be able to catch these failure modes and timely repairs can fix them before the equipment breaks down.

Comparative analytics technology has the ability to analyze data from various companies by equipment type and generate a list of dominant failure modes by cost and count. In Figure 4, the utility medium leakage and vibration are the top two most frequent failure modes for steam turbines. Whereas, utility medium leakage and erratic output are the costliest failure modes. The PM and PdM tasks created for steam turbines need to focus on checking for these failure modes to reduce the need for reactive repairs.

Implementing an asset performance management solution

By implementing an effective comparative analytics and an APM solution, analysts have the ability to identify three specific groups of assets using peer performance, including: assets that have more than average proactive work, yet still experience more than average reactive work; assets that have less than average proactive work and more than average reactive work; and assets that have more than average proactive work and less than average reactive work. Each of these categories warrant actions to increase the efficiency of PMs and PdMs by targeting the dominant failure modes and reducing the non-value added tasks. Leveraging comparative analytics, organizations can achieve operational excellence while improving safety, reducing risk and driving continuous improvement by quickly providing key insights into industrial assets.

Failures of Steam Turbines
Top 5 failure modes by cost and count for steam turbines.

Predix Asset Performance Management

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Predix Asset Performance Management (Predix APM) is a suite of software and service solutions designed to help optimize the performance of your assets. Predix APM increases asset reliability and availability while optimizing
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About the author

Manjish Naik

Project Manager, Asset Answers, GE Digital

Manjish Naik is a project manager for Asset Answers, a benchmarking service within Predix Asset Performance Management that enables instant comparison of industry asset performance, in terms of cost, reliability and availability.

Manjish works to facilitate successful and efficient inclusion of customer data in the Asset Answers database. He conducts value consultations with clients to provide asset performance analysis and improvements by using metrics and benchmarks in Asset Answers. He also provides expertise for new product enhancements and training content development. He does product demonstrations for prospective clients during pre-sales engagements.

Previously, Manjish worked as a Consultant for Safety Instrumented Systems and Calibration Management and was responsible for working with various clients to recommend implementations based on GE Digital workflows and Best Practices.  

Before he joined GE Digital, Manjish worked for Honeywell and Emerson, as a Control Engineer for process automation. He holds a B. Tech. degree in Instrumentation & Controls Engineering from India and an M.S. degree in Electrical Engineering from Arizona State University. 

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