Captured by Data Part 2

by Mr. Mather
Posted 8-1-06

Designing maintenance policy

When maintenance policy designers begin to develop a management program they are almost always confronted with a lack of reliable data to base their judgments on. It has been the experience of the author that most companies start reliability initiatives using an information base that is made up of approximately 30% hard data, and 70% of knowledge and experience.
One of the leading reasons for this is the nature of critical failures and the response they provoke. However there are often other factors such as data capturing processes, consistency of the data, and the tendency to focus efforts in areas that are of little value to the design of maintenance policy. With EAM technologies changing continually, there are often upgrade projects, changeover projects, and other ways that data can become diluted.

There are still other key reasons why data from many EAM implementations are of limited value only. Principal among these is the fact that even with well-controlled and precise business processes for capturing data, some of the critical failures that will need to be managed may not yet have occurred. An EAM system, managing a maintenance program that is either reactive or unstructured, will only have a small impact on a policy development initiative.
At best they may have collected information to tell us that faults have occurred, at a heavy cost to the organization, but with small volumes of critical failures and limited information regarding the causes of failure. RCM facilitates the creation of maintenance programs by analyzing the four fundamental causes of critical failures of assets, namely:

1. Poor asset selection (Never fit for purpose)
2. Asset degradation over time (Becomes unfit for purpose)
3. Poor asset operation (Operated outside of the original purpose)
4. And, exceptional human errors (Generally following the GEM principles)

The RCM Analyst needs to analyze all of the reasonably likely failure modes in these four areas, to an adequate level of detail. Determining the potential causes for failures in these areas, for a given operating environment, is in part informed by data, but the vast majority of the information will come from other sources.
Sources such as operators’ logs are strong sources for potential signs of failure, as well as for failures often not found in the corporate EAM. Equipment manufacturers’ guides are also powerful sources for gleaning information regarding failure causes and failure rates. However, all information from a manufacturer needs to be understood in the context of how you are using the asset, and the, often conservative, estimates of the manufacturer. For example, if there are operational reasons why your pumping system is subject to random foreign objects, for whatever reason, then failure rates for impeller wear can become skewed.
Other sources of empirical data can be found in operational systems such as SCADA or CITECT, commercial databanks, user groups, and at times consultant organizations. Similarly to information from manufacturers there is a need to understand how this applies to the operating environment of your assets. As asset owners require more and more technologically advanced products, items come onto the market with limited test data in operational installations, further complicating the issues of maintenance design through data.
The factors that decide the lengths that an RCM Analyst should go to collect empirical data is driven by a combination of the perceived risk, (probability x consequence), and of course the limitations set on maintenance policy design by commercial pressures. Even when all barriers are removed from the path of the RCM Analyst, they are often confronted faced with an absence of real operational data on critical failures.
The vast majority of the information regarding how the assets are managed, how they can fail, and how they should be managed, will come from the people who manage the assets on a day-to-day basis. Potential and historic failure modes, rate of failure, actual maintenance performed (not what the system says, but what is really done), why a certain task was put into place in the first place, and the operational practices and reasons why, are all elements of information that are not easily found in data, but in knowledge.
This is one of the overlooked side-benefits of applying the RCM process, that of capturing knowledge, not merely data. As the workforce continues to age, entry rates continue to fall in favor of other managerial areas, and as the workforce becomes more mobile, the RCM process, and the skills of trained RCM Analysts, provides a structured method to reduce the impact of diminishing experience.