The first change is the perception that PdM is exclusively a maintenance management tool. This change must permeate throughout the plant. While this sounds simple, changing corporate attitudes toward PdM is difficult. Most corporate-level managers have little knowledge of maintenance. Convincing them to make broader use of predictive technologies can be extremely difficult. Some simply can't understand that the majority of failures are the result of non-maintenance issues.
From our studies during the past 30 years, maintenance is responsible for only about 17% of production interruptions and quality problems. Inappropriate operating practices, poor design, non-specification parts and other non-maintenance reasons are to blame for the rest.
In reality, predictive technologies are plant and process optimization tools. They detect, isolate and provide solutions for deviations that vaporize capacity, degrade quality, introduce cost and threaten employee safety. Shift your predictive technologies from the maintenance department to a reliability group charged with responsibility and accountability for plant optimization. This group must have the authority to cross functional boundaries freely to implement changes that will correct the problems their evaluations uncover.
This is a radical departure from the traditional structure. As a result, expect resistance from the top to the bottom of the organization. With the exception of those few enlightened employees who understand, most of the workforce won't openly embrace this new functional group. However, the formation of a dedicated group solely responsible for reliability improvement and optimization of every plant operation is essential. It's the only way a plant or corporation can achieve and sustain world-class performance.
Staffing this group won't be easy. The team must have thorough knowledge of machine and process design, as well as best practices in both the operation and maintenance of critical plant production systems. The group must understand engineering methods that provide the lowest life cycle cost. Finally, the team must understand the proper use of predictive technologies. Few plants have employees who exhibit all these fundamental traits.
There are two ways to resolve this problem. First, select personnel who've already mastered one or more of these disciplines.
For example, the group might consist of the best person from operations, maintenance, engineering and predictive technology. Ensure that each group member has deep knowledge of one or more specialty areas. Organizational superstars may not have in-depth knowledge of their specialties. In other words, the best operator may be the worst contributor to reliability or performance.
Following this approach requires that training be the first priority. Few have all the knowledge and skills their function requires -- a fact that's especially true. Therefore, provide sufficient training to ensure maximum return on your investment. Training should focus on process or operating dynamics for each critical production system in the plant. It should include comprehensive process design, operating envelope, operating methods and process diagnostics training that form the foundation for the ability to optimize performance.
The second approach is to hire professional reliability engineers. While it sounds easier, it isn't necessarily so. First, there are few fully qualified reliability professionals available, and they're very expensive. Most prefer to work as short-term consultants rather than as long-term employees. If you try to hire from the outside, use extreme caution. Resumes may read well, but knowledge is hard to find. As an example, we recently interviewed 150 so-called qualified predictive engineers, but found only five with the basic knowledge we sought. They required training before they could provide acceptable performance.
Using PdM properly
System components must operate within their own design envelopes before the overall system can meet its designed performance level. Why then, do most predictive programs treat components as isolated machine trains? Instead of evaluating a centrifugal pump or gearbox as part of the total machine, most predictive analysts limit their technology to simple diagnostics of the mechanical condition of the individual component. As a result, no effort is made to determine the influence of system variables, such as load, speed, product and instability on the individual component. Fluctuations in process variables often are the cause of problems in the pump or gearbox. Unless the analyst considers them, it's difficult to determine the root cause. Instead, you'll get a recommendation that corrects the symptom - damaged bearing or misalignment - rather than the real problem.
The converse is also true. When diagnostics are limited to individual components, system problems can't be detected, isolated and resolved. Remember, it's the system, not the component, that generates capacity, revenue and bottom-line profit for the plant. The system, therefore, must be the primary focus of any analysis.
When one thinks of PdM, the first things that come to mind are vibration monitoring, thermography and tribology, but they aren't the panacea for plant problems. These cornerstones of predictive technology can't provide the diagnostics required for achieving and sustaining world-class performance levels. To gain maximum benefit from PdM, more change is needed.
Tracking process variables, such as flow rates, retention time, temperatures and others, is a requirement for every predictive maintenance and process optimization program. These variables define the operating envelope of the process and are essential requirements for operating the system. In many cases, these data are readily available.
On systems that use computer-based or PLC controls, the variables that define its operating envelope are acquired automatically to allow the control logic to operate the system. The type and number of variables varies from system to system, but are based on the design and mode of operation for that specific type of production system. It's a relatively simple matter to acquire these data from the Level I control system and then use them as part of the predictive diagnostic logic. In most cases, these data, combined with traditional predictive technologies, provide the information to fully understand a system's performance.
Don't ignore manually operated systems. While the relevant process data are more difficult to obtain, the reliability or predictive analyst can, in most cases, acquire enough to permit a full diagnosis of the system's performance or operating condition. Analog gauges, thermocouples, strip chart recorders and other traditional plant instrumentation can be used for this purpose. If plant instrumentation includes an analog or digital output, most microprocessor-based vibration meters can acquire proportional signal outputs directly and automate the data manipulation required for this expanded scope of predictive technology.
Contributing Editor R. Keith Mobley is principal consultant at Life Cycle Engineering in Charleston, S.C.
E-mail him at kmobley@LCE.com.