- By Jason Andersen
- Web Exclusive
Summary
Perhaps the world's best predictive maintenance system is in place at a large petrochemical complex in the Houston Ship Channel. This system, however, is not a piece of software. Instead, it is a reliability and maintenance engineer, we will call him Carl, with 40-plus years of experience. His story was related to me at a recent industry event, and he is responsible for optimizing maintenance of 150 critical pumps.
Carl does this by visiting each pump once a week, clipboard in hand, recording data from gauges and comparing it to past readings. He augments this data with aural input gathered by listening, and with vibration input acquired by laying hands on each pump. He combines this input with knowledge of plant operations, including expected day to day variances.
The result is an extremely accurate picture of pump health, from which Carl creates a maintenance schedule. His actions ensure each pump is serviced just when it needs to be, and not too frequently, as this would drive up costs, but in all cases before failure.
Carl's activities are critically important. Studies show pumps are very maintenance-intensive equipment, suffering failures or some level of degraded operation about once a year. Fixing these problems before they occur is extremely important, because reactive maintenance, repairing something after failure, costs 50 percent more than predictive maintenance, which detects and addresses problems before failure.
Perhaps your plant or facility has its own "Carl," or maybe quite a few of them. These employees are vital to efficient operation, and you may spend quite a bit of time wondering just how you can capture their knowledge, store it, and transfer it to other employees when the time comes.
And just when is this time? In the past, many of these workers retired on a fairly predictable schedule driven by their defined benefit pension plan. Their replacements would be waiting in the wings, with staffing levels sufficient to allow training and knowledge transfer over a predictable period of time.
Nowadays, with most workers relying on 401(k) and other defined contribution plans, many work longer, and as these workers age, their retirement dates become more uncertain. Compounding this issue, staffing levels at most every industrial plant or facility are below what they once were, making it more difficult for junior workers to cross-train with experienced employees. This crucial overlap time is now often an unknown interval, which could be years, or maybe just a few months.
There are two main ways to address this issue. The first is to keep doing things as they have always been done, but with a new person. Carl trains his replacement, bestowing a clipboard and the requisite training.
The second approach is to automate some or all of Carl's activities by collecting data with instruments, instead of manually reading gauges, and transmitting this data to analytics software to generate results and optimize maintenance. Carl trains his replacement to make final decisions.
The first method, continuing to work as before but with a new person, presents several issues. First, it is hard to find a Carl, and it can take many years to train him or her. Second, the new employee will probably be much younger than Carl and will naturally balk at using antiquated methods of data collection, such as a clipboard. Third\, the required training time will be quite unpredictable, as it will depend heavily on the new employee's aptitude and experience. Finally, Carl's actual retirement date will probably be unknown, making it impossible to judge the best time to hire and fully train Carl's eventual successor.
The second technique is automating the activity to the greatest extent possible and practical. For pump predictive maintenance, this means installing instruments to monitor condition and performance. Problems are detected as soon as possible, so appropriate action can be taken. Typical pump parameters monitored include flow, vibration, bearing temperature, inlet/outlet pressure, power consumption, and seal fluid pressure/level.
Many of these condition monitoring instruments are now available in wireless versions, which can cut the cost and time of installation by up to 50 percent. Whether wired or wireless, these instruments provide raw data to analytics software, which can be used to predict problems. Some vendors offer analytics designed specifically for evaluating pump performance, and this special-purpose software is easier to use than general-purpose platforms.
Results from analytics software can then be correlated with expert opinion to indicate whether a problem exists or not, along with the recommended course of action. For example, if the pump flow decreases on a Tuesday, it is not an issue, because it is the result of an expected regular weekly change to the product mix. But if the same condition occurs on a Thursday, it is an issue. This type of information can become part of the analytics software configuration, increasing its intelligence and effectiveness.
Companies establishing automated equipment monitoring systems will find that new employees can be brought up to speed in weeks or months instead of years. And by applying this approach to other types of assets and equipment, an employee can become extremely productive in many areas as he or she combines the power of automation with growing knowledge of plant operations.
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