“If you can find a path with no obstacles, it probably doesn’t lead anywhere.” — Frank A. Clark
"So easy that even a caveman can do it,” as stated in a popular TV commercial, could easily be used to describe today’s predictive maintenance tools because they work so well. However, to be truly competitive, a company’s goals should go further than being satisfied with marginal improvements in machine reliability.
Achieving equipment reliability that is required for maximum profits is both realistic and obtainable for any company. Proper use of predictive maintenance (PdM) tools is a key factor in realizing such goals. This article provides solutions to overcoming obstacles and issues associated with monitoring machinery and using predictive maintenance tools—such as precision shaft alignment and vibration instruments.
Deciding who receives training is important. Many times predictive maintenance technicians are the only people trained in using predictive maintenance technologies. This can sometimes put them at odds with frontline supervisors who may not understand the importance of such methods.
A solution is to train these supervisors in the basics of the predictive maintenance technologies used in the plant. Excellent, cost-effective training can usually be obtained from the equipment vendors. All stakeholders in machine reliability should have a thorough understanding of the benefits and uses of predictive technologies.
Poorly written or non-existent reports are another factor that may limit predictive maintenance success.
If detected faults are only reported by word of mouth, they may fall through the cracks and not be acted upon. Good, concise reports that provide monetary savings and correctly state problems in an easy-to-understand format are important.
A good technician producing poor reports will not be successful.
Alarms that are not accurately set will allow for missed faults or result in crying “wolf” when no faults are present. Predictive maintenance software is so well-developed that the computer can be good at gleaning faults. However, this can only happen if alarm levels are properly set. The curve fitting features of some software may even provide a good estimate of when a measured parameter may go into an alarm condition.
Technicians may not know all the features of the software they use and may also not make use of every feature that could benefit their predictive maintenance process. An alarm should be thought of as a tolerance standard. Without standards, end users cannot know the condition of their equipment. An alarm standard can be treated as a measure of machine reliability.
Adequate and Organized Data Collection
Inadequate data collection may also result in poor predictive maintenance work. Attention to detail in data collection is paramount to success. There should never be any doubt about where and how previous data was collected. If those parameters are not known, the measurement is not repeatable to any degree of confidence.
No aspect of predictive maintenance is more important than data collection. Only good data provides information for making good decisions. Sometimes, technicians may not keep their databases current. This can make the predictive maintenance process out of date if current information is not readily available.
Within a week, new information will require that the databases are tweaked, whether a new machine is installed or an alarm setting is adjusted. Conducting predictive maintenance with out-of-date information can lead to making the wrong reliability decisions.
Lack of Compliance and Sustainability
Too often a company or plant will achieve success through their predictive maintenance efforts only to slide back into the bad habits of poor maintenance. Gaining initial success is usually easier than sustaining the effort. Just knowing this fact can help an organization continually improve its predictive maintenance efforts. One of the causes of this regress is bringing new managers into the organization who are not well-versed in the benefits and methods of predictive maintenance strategies. This is simply a training issue that can easily be addressed through proper training and by having a well-documented physical asset management strategy.
Another reason for such regress is that managers new to the organization may be biased toward an entirely different asset management strategy, and they may want to toss out the old and bring in the new. Managers are brought into the organization with hopes that they bring new ideas with them. However, these new ideas should not be implemented to the detriment of established processes that have been proven successful. The best option in these cases is to have those new to the organization incorporate their ideas into the working process. The goal would be for the new input to add value to the strategies already in place and not to replace current, successful predictive maintenance processes.
Root Cause Analysis & Repair
Another obstacle is the employment of predictive maintenance tools that only detect defective parts, creating a vicious cycle of doctoring symptoms without addressing the root causes of failure. This is a common practice in many organizations. Good predictive maintenance is about solving problems. Predictive maintenance tools are a great benefit in helping identify the root causes of failures.