Problems to fix before establishing an effective PdM program

Establish your predictive maintenance program after eliminating these problems

Implementing an effective predictive maintenance program should be relatively simple and straightforward. After all, it's simply a matter of selecting the right technologies, procuring a suitable system, building an asset database, acquiring a baseline or benchmark data set, and then maintaining the program. What's the big deal?

Unfortunately, establishing an effective predictive maintenance program can be a bit more complicated that it appears. The effort must address the more serious problems that a plant attempting to add a predictive maintenance program faces routinely.

Is PdM justified?
The first question is whether predictive maintenance adds real value to an effective maintenance program. In most cases, the answer is yes, but predictive maintenance is not a panacea for every every plant. The first step is to determine whether critical plant assets lend themselves to predictive maintenance.

This requires certain essential tasks a comprehensive criticality analysis to define the relative importance of each asset to plant functionality and a simplified failure modes and effects analysis to define specific failure modes.

The next step is to determine whether predictive technologies can identify these failure modes early enough. If so, predictive maintenance may be justified. If not, implementing it won't provide real value. Too many plants select predictive technologies without any real idea whether they can be used effectively. As a result, such programs provide little value.

Select the technologies
Identifying the most effective predictive technology for your plant is the second obstacle. It's a decision that must be based on a detailed cost-benefit analysis. For example, vibration monitoring might be the best technical choice, but the higher recurring cost of data acquisition and analysis might be prohibitive. If so, ultrasonics or improved visual inspection may be more cost-effective. While the latter choices can't provide the same level of analysis vibration testing, they offer the ability to prevent unexpected failure of critical components.

With the appropriate technologies selected, the next hurdle is purchasing the best site-specific predictive maintenance systems. It can be difficult for a first-time user to determine the real strengths and weaknesses of the systems. Each is promoted as being able to provide the capabilities needed to support every application in every plant, but, unfortunately, this claim is simply not true.

For example, the price range for the instrument and software in a fully functional infrared scanning system is between $9,000 and $60,000. The less expensive system provides most of the capabilities and supports most predictive maintenance programs effectively. The cost of most vibration monitoring systems ranges between $20,000 and $30,000 for something that appears to provide universal capabilities. However, the response characteristics, such as low-frequency capability, dynamic range, bandwidth, resolution, narrow-band filtration and data conversion, vary greatly.

The predictive maintenance systems must match the unique requirements of each plant and each critical asset. Each plant must clearly understand the unique requirements of its critical assets before attempting to select predictive maintenance systems.

Training
The average predictive maintenance analyst receives between five and 15 days of formal training when a company first establishes a predictive maintenance program. The vendor normally provides five days of training limited to a basic introduction to the technology and product, and how to use the basic functions. Armed only with this knowledge, the analyst is expected to establish an effective, comprehensive database that will form the basis for the predictive maintenance program. Based on my experience, this level of knowledge is insufficient to provide any hope of ever having an effective database. The majority of programs established in this manner fail to provide any real benefits and most are discontinued within the first two years.

A fortunate few analysts receive an additional two to three weeks of specific training from third-party companies that specialize in intermediate and advanced training. While some such courses provide a better technical understanding, most lack the practical knowledge that new analysts need.

Use PdM information
Perhaps the biggest obstacle to success is when a plant's management team elects not to use the information its predictive maintenance team generates. For some reason, too many plant managers seem to rely more on the instincts of maintenance technicians than on the scientific data that predictive maintenance technology provides. Not too long ago, a maintenance manager questioned why a contractor providing vibration monitoring services recommended changing more than 500 bearings.

Upon investigation, the manager discovered that the vibration contractor had recommended changing only five bearings; the rest had been changed because the in-house maintenance technicians felt that they didn't sound or feel right.

The other problem is that too many plants place the predictive maintenance program recommendations much lower on the priority list than routine preventive, scheduled rebuilds and other work requests. It seems that the predictive tasks are performed only when there is time left after other tasks are completed. This doesn't seem to make much sense, but old habits are difficult to break.

Although other obstacles limit the successful application of predictive maintenance, the few here are almost universal. Unless maintenance technicians anticipate problems and make every effort to eliminate, or at least mitigate, their impact, there is little chance that a company will derive real benefits by adopting predictive maintenance. The good news is that you can eliminate these obstacles with minimal effort. The added benefits will more than offset the incremental cost. 


Contributing Editor R. Keith Mobley is principal consultant for Life Cycle Engineering in Charleston, S.C. E-mail him at kmobley@LCE.com.

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