Introduction
Among the areas where modern enterprise asset management (EAM) systems provide substantial benefits is the driving out of inefficiencies in business processes. Through the capture, storage, manipulation, and display of historical transactional data, companies can take great leaps forward in the efficiency with which they execute maintenance programs. They can do this, for example, through ensuring that delays in executing work are captured, analyzed, and resolved, or by being able to display trends in performance and cost over time.
Part Two of the series Captured by Data.
The effectiveness of a maintenance task comes from how it manages failure modes, not from the level of efficiency that it is executed with. The original reliability-centered maintenance (RCM) studies revealed that many routine tasks could actually contribute to failure, or to lower cost-effectiveness, by having limited or no impact on the performance of the asset (in effect wasting the maintenance budget). Executing these tasks with greater efficiency would have either have no impact at all on effectiveness, or would possibly even magnify the effects of unsuitable tasks.
For example, after an RCM analyst had spent a lot of time working with a utility company in the UK, it became clear that the reported schedule compliance was not an accurate figure. Schedules were regularly coming in with 100 percent compliance, while the reality was that they were actually performing at around 25 percent.
After some investigation it turned out that the crafts people recognized that most of the regimes that were coming out of the system were either counterproductive, or not applicable at all. So they were fortunately omitted. Prior to installing the EAM system, they were working with job cards in separate systems; once the EAM went "live," these were collated and assigned to all similar assets regardless of operational context.
This is where RCM-style methodologies contribute to the modern EAM or computerized maintenance management system (CMMS) system. By providing the content that the system needs to manage, they are ensuring that the right job is being executed in the right way. This is common sense, and practitioners of RCM have been emphasizing this point for many years.
What is often not emphasized, however, is that having an effective maintenance program in place which is integrated with the EAM system ensures that future efforts of data capture are executed in a manner that supports the principles of responsible asset stewardship. The effect of building a data capture program on the back of an effective maintenance program is to reverse, over time, the ratio of hard data to human knowledge that is available for decision making.
Figure 1. Integration of EAM and reliability-centered maintenance
On performing analysis, the structured approach within the decision diagram drives RCM analysts to develop an asset management program that is practical, cost effective, and tailored to a given level of performance and risk. There are two main outputs for any correctly performed analysis. The first is one-off changes to procedures, software, asset configurations, asset types, company policies, and asset designs.
The second area is a group of routine maintenance tasks designed to manage the failure mode under analysis. Aside from combinations of policies, RCM supports five different maintenance policy options, as detailed below. These make up the bulk of the content that the EAM system is installed to manage (the strategy or policy options offered within RCM are detailed in the RCM standard SAE JA1011).
1. Predictive maintenance (PTive): a task to predict when a failure mode is about to occur.
2. Preventive restoration (PRes): a task to prevent failure through applying a task, at a time or usage based interval, to restore the assets' original resistance to failure.
3. Preventive replacement (PRep): a task to prevent failure through replacing an asset or component, at a time or usage based interval.
4. Detective maintenance (DTive): a task to detect whether an item has failed or not. This task is only applied to failure modes that RCM classifies as hidden.
5. Run-to-fail (RTF): a policy to allow an asset to fail, rather than applying any form of routine maintenance. Failure modes that are allowed to run to failure have low, or negligible, consequences in terms of cost only. These are the non-critical, or acceptable, failures that were referred to earlier in this document.
An RCM-based process selects these tasks based on their applicability and effectiveness, as defined within the decision algorithms. These issues have been commented on many times and will not be dealt with in great detail within this paper.
For modern RCM analysts, the routine maintenance tasks are of interest not only because of the impact they have on asset performance, but also because of the way they can be used to develop the asset information portfolio, contribute to whole-of-life costing, and provide an additional tool for proactive monitoring of asset performance and corporate risk exposure.
As with the logic of the decision diagram, the criteria and characteristics of each of these policy choices have been detailed many times, and it is not necessary to describe them in detail here. However, it is necessary to detail how they affect the collection, management, and use of dynamic asset data.
Predictive Maintenance
As detailed in figure 2 below, predictive maintenance (PTive) tasks are established to try to detect the warning signs that indicate the onset of failure, thus allowing for actions to be taken to avoid the failure. Yet there is also another aspect of PTive tasks that is often overlooked: that of the corrective or predicted (PTive) task once warning signs have been detected.
Immediately following the analysis, the information established can be used for creating proactive whole-of-life costing models that are directly tied to performance and risk.
Figure 2. Tasks involved in predictive maintenance
The whole-of-life cost of an asset, or component, subject to predictive maintenance tasks, can be represented by the following equation:
whole-of-life cost = (cost (PTive) x n) + cost (PTive),
where n represents the number of times the PTive task is likely to be executed. This also drives estimates of the time between installation and likely failure. It needs to be recognized that the corrective or PTive task is executed at a time less than end-of life (although small).
As time passes, the amount of data that is collected on these tasks will grow, collected now in a responsible manner, and can also be used in statistical models regarding asset degradation and predictions of capital spend requirements. By the inclusion of these outputs of an RCM analysis, asset managers can use the results with increasing confidence as predictors of whole-of-life cost profiles, and end-of-life points.
Preventive Maintenance
Where predictive maintenance tasks cannot be applied, for whatever reason, the next two options on either side of the decision diagram are preventive maintenance tasks. These are tasks that are aimed at either restoring an asset's resistance to failure (PRes), or at replacing the asset at a time before the failures can occur (PRep), thus preventing failures. These tasks have limited use and are based on age, usage, or some other representation of time.
Figure 3. Tasks involved in preventive maintenance
When applied correctly, these tasks are part of the approach to maintenance that, by necessity, reduces the volume of failure data available for statistical analysis. However, with the component out of the operational environment, it can safely be tested to try to establish the extent of its remaining economically useful life.
The whole-of-life cost of an asset, or component, subject to preventive maintenance tasks, can be represented by the following equation:
whole-of-life-cost = cost (PRes) or cost (PRep)
This is an additional task, one that would not be generated from the RCM analysis. Yet it represents another aspect of responsible data capture, and is an important element of businesses where confidence in statistical life prediction, and whole-of-life costing models, are of importance. This could theoretically, be suitable for all companies that need to manage physical assets. However, it has particular importance for financially regulated institutions and companies that need to prove the case for funding.
Detective Maintenance
As with predictive maintenance tasks, there are actually two tasks that are being implemented in detective maintenance: first, the detective (DTed) maintenance task, and second, the detected (DTed) maintenance task. The result of this is the same as with the predictive maintenance tasks. That is, it provides further information about the likely failure rate, collected in a responsible manner, which can be used to inform decisions regarding optimization of the frequency of this task.
Figure 4. Tasks involved in detective maintenance
The whole-of-life cost of an asset, or component, subject to detective maintenance tasks, can be represented by the following equation:
whole-of-life cost = (cost (DTed) x n) + cost (DTed),
where n represents the number of times the DTed task is likely to be executed. This also drives estimates of the time between installation and likely failure. It needs to be recognized that the corrective or DTed, task is executed at a time greater than end-of life due to the characteristics of this task. As time passes, the data collected can be used to inform decisions and whole-of-life models with increasing certainty.
This is particularly relevant for hidden failures, or hidden functions as they are sometimes called. When implementing the outcomes of an RCM analysis, some of the tasks are DTed tasks. That is, they are tasks put in place to detect if a failure has occurred. Often, the items being tested have not been tested for a long period of time—sometimes years. And often nobody knows if they are working or not!
So when establishing the initial DTed task frequencies, often the information used is not very certain, and is backed by only the experiences and memories of those involved in the exercise. Fortunately, manufacturers do often have a good level of information regarding failure rates in these sorts of devices. But the result is till quite conservative, and not tuned for the specific operational climate. Performing DTed tasks will immediately help the company to establish some baseline information regarding failure rates of the device.
SOURCE:
http://www.technologyevaluation.com/research/articles/asset-data-for-accurate-lifecycle-management-18686/
Among the areas where modern enterprise asset management (EAM) systems provide substantial benefits is the driving out of inefficiencies in business processes. Through the capture, storage, manipulation, and display of historical transactional data, companies can take great leaps forward in the efficiency with which they execute maintenance programs. They can do this, for example, through ensuring that delays in executing work are captured, analyzed, and resolved, or by being able to display trends in performance and cost over time.
Part Two of the series Captured by Data.
The effectiveness of a maintenance task comes from how it manages failure modes, not from the level of efficiency that it is executed with. The original reliability-centered maintenance (RCM) studies revealed that many routine tasks could actually contribute to failure, or to lower cost-effectiveness, by having limited or no impact on the performance of the asset (in effect wasting the maintenance budget). Executing these tasks with greater efficiency would have either have no impact at all on effectiveness, or would possibly even magnify the effects of unsuitable tasks.
For example, after an RCM analyst had spent a lot of time working with a utility company in the UK, it became clear that the reported schedule compliance was not an accurate figure. Schedules were regularly coming in with 100 percent compliance, while the reality was that they were actually performing at around 25 percent.
After some investigation it turned out that the crafts people recognized that most of the regimes that were coming out of the system were either counterproductive, or not applicable at all. So they were fortunately omitted. Prior to installing the EAM system, they were working with job cards in separate systems; once the EAM went "live," these were collated and assigned to all similar assets regardless of operational context.
This is where RCM-style methodologies contribute to the modern EAM or computerized maintenance management system (CMMS) system. By providing the content that the system needs to manage, they are ensuring that the right job is being executed in the right way. This is common sense, and practitioners of RCM have been emphasizing this point for many years.
What is often not emphasized, however, is that having an effective maintenance program in place which is integrated with the EAM system ensures that future efforts of data capture are executed in a manner that supports the principles of responsible asset stewardship. The effect of building a data capture program on the back of an effective maintenance program is to reverse, over time, the ratio of hard data to human knowledge that is available for decision making.
Figure 1. Integration of EAM and reliability-centered maintenance
On performing analysis, the structured approach within the decision diagram drives RCM analysts to develop an asset management program that is practical, cost effective, and tailored to a given level of performance and risk. There are two main outputs for any correctly performed analysis. The first is one-off changes to procedures, software, asset configurations, asset types, company policies, and asset designs.
The second area is a group of routine maintenance tasks designed to manage the failure mode under analysis. Aside from combinations of policies, RCM supports five different maintenance policy options, as detailed below. These make up the bulk of the content that the EAM system is installed to manage (the strategy or policy options offered within RCM are detailed in the RCM standard SAE JA1011).
1. Predictive maintenance (PTive): a task to predict when a failure mode is about to occur.
2. Preventive restoration (PRes): a task to prevent failure through applying a task, at a time or usage based interval, to restore the assets' original resistance to failure.
3. Preventive replacement (PRep): a task to prevent failure through replacing an asset or component, at a time or usage based interval.
4. Detective maintenance (DTive): a task to detect whether an item has failed or not. This task is only applied to failure modes that RCM classifies as hidden.
5. Run-to-fail (RTF): a policy to allow an asset to fail, rather than applying any form of routine maintenance. Failure modes that are allowed to run to failure have low, or negligible, consequences in terms of cost only. These are the non-critical, or acceptable, failures that were referred to earlier in this document.
An RCM-based process selects these tasks based on their applicability and effectiveness, as defined within the decision algorithms. These issues have been commented on many times and will not be dealt with in great detail within this paper.
For modern RCM analysts, the routine maintenance tasks are of interest not only because of the impact they have on asset performance, but also because of the way they can be used to develop the asset information portfolio, contribute to whole-of-life costing, and provide an additional tool for proactive monitoring of asset performance and corporate risk exposure.
As with the logic of the decision diagram, the criteria and characteristics of each of these policy choices have been detailed many times, and it is not necessary to describe them in detail here. However, it is necessary to detail how they affect the collection, management, and use of dynamic asset data.
Predictive Maintenance
As detailed in figure 2 below, predictive maintenance (PTive) tasks are established to try to detect the warning signs that indicate the onset of failure, thus allowing for actions to be taken to avoid the failure. Yet there is also another aspect of PTive tasks that is often overlooked: that of the corrective or predicted (PTive) task once warning signs have been detected.
Immediately following the analysis, the information established can be used for creating proactive whole-of-life costing models that are directly tied to performance and risk.
Figure 2. Tasks involved in predictive maintenance
The whole-of-life cost of an asset, or component, subject to predictive maintenance tasks, can be represented by the following equation:
whole-of-life cost = (cost (PTive) x n) + cost (PTive),
where n represents the number of times the PTive task is likely to be executed. This also drives estimates of the time between installation and likely failure. It needs to be recognized that the corrective or PTive task is executed at a time less than end-of life (although small).
As time passes, the amount of data that is collected on these tasks will grow, collected now in a responsible manner, and can also be used in statistical models regarding asset degradation and predictions of capital spend requirements. By the inclusion of these outputs of an RCM analysis, asset managers can use the results with increasing confidence as predictors of whole-of-life cost profiles, and end-of-life points.
Preventive Maintenance
Where predictive maintenance tasks cannot be applied, for whatever reason, the next two options on either side of the decision diagram are preventive maintenance tasks. These are tasks that are aimed at either restoring an asset's resistance to failure (PRes), or at replacing the asset at a time before the failures can occur (PRep), thus preventing failures. These tasks have limited use and are based on age, usage, or some other representation of time.
Figure 3. Tasks involved in preventive maintenance
When applied correctly, these tasks are part of the approach to maintenance that, by necessity, reduces the volume of failure data available for statistical analysis. However, with the component out of the operational environment, it can safely be tested to try to establish the extent of its remaining economically useful life.
The whole-of-life cost of an asset, or component, subject to preventive maintenance tasks, can be represented by the following equation:
whole-of-life-cost = cost (PRes) or cost (PRep)
This is an additional task, one that would not be generated from the RCM analysis. Yet it represents another aspect of responsible data capture, and is an important element of businesses where confidence in statistical life prediction, and whole-of-life costing models, are of importance. This could theoretically, be suitable for all companies that need to manage physical assets. However, it has particular importance for financially regulated institutions and companies that need to prove the case for funding.
Detective Maintenance
As with predictive maintenance tasks, there are actually two tasks that are being implemented in detective maintenance: first, the detective (DTed) maintenance task, and second, the detected (DTed) maintenance task. The result of this is the same as with the predictive maintenance tasks. That is, it provides further information about the likely failure rate, collected in a responsible manner, which can be used to inform decisions regarding optimization of the frequency of this task.
Figure 4. Tasks involved in detective maintenance
The whole-of-life cost of an asset, or component, subject to detective maintenance tasks, can be represented by the following equation:
whole-of-life cost = (cost (DTed) x n) + cost (DTed),
where n represents the number of times the DTed task is likely to be executed. This also drives estimates of the time between installation and likely failure. It needs to be recognized that the corrective or DTed, task is executed at a time greater than end-of life due to the characteristics of this task. As time passes, the data collected can be used to inform decisions and whole-of-life models with increasing certainty.
This is particularly relevant for hidden failures, or hidden functions as they are sometimes called. When implementing the outcomes of an RCM analysis, some of the tasks are DTed tasks. That is, they are tasks put in place to detect if a failure has occurred. Often, the items being tested have not been tested for a long period of time—sometimes years. And often nobody knows if they are working or not!
So when establishing the initial DTed task frequencies, often the information used is not very certain, and is backed by only the experiences and memories of those involved in the exercise. Fortunately, manufacturers do often have a good level of information regarding failure rates in these sorts of devices. But the result is till quite conservative, and not tuned for the specific operational climate. Performing DTed tasks will immediately help the company to establish some baseline information regarding failure rates of the device.
SOURCE:
http://www.technologyevaluation.com/research/articles/asset-data-for-accurate-lifecycle-management-18686/
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