Unfortunately, maintenance decisions in many plants still rely on a clear line of sight or other obvious landmarks to determine priority. Precious maintenance resources are spent reacting to immediate conditions that negatively affect production. Maintenance managers use daily emergencies to navigate maintenance activities. Today’s maintenance management often resembles the pilot practices of yesterday.

Proactive techniques such as reliability centered maintenance (RCM) were originally designed to ensure reliable aircraft operation and have recently been applied to industrial maintenance management. In many cases, RCM treats the industrial plant like an aircraft and requires that all systems, no matter how insignificant, be evaluated for possible failure modes. Addressing any and all failure modes can require a great deal of time and energy from subject matter experts (who are usually already busy doing their normal jobs). It is during this resource-intensive process that many RCM initiatives fail.

However, if you want a great deal of bang for the reliability buck you may want to look at some of the statistical methods for reliability from noted experts such as Dr. Robert Abernethy, Wes Fulton, and Paul Barringer. Fortunately, these enlightened professionals have published web sites with a virtual treasure trove of information and resources.

Statistical reliability approaches focus your efforts on the failure modes that cost the most money, separating the vital few from the trivial many like a laser-guided missile.

Barringer has more than 35 years of engineering and manufacturing experience in design, production, quality, maintenance, and reliability of technical products. Note his experience in both the technical and bottom-line aspects of operating a business with an understanding of how reliable products and processes contribute to financial business success.

According to Barringer, “Reliability and money are a wonderful combination—one hand washes the other. The problem of life cycle cost is to know when things fail so you can price-out the failure costs with an Excel spreadsheet for life cycle cost considering the time value of money in NPV calculations. You find when things will fail by exercising the reliability calculations.”

The Barringer & Associates web site features a generic Weibull database for many popular systems as well as a challenging “Problem of the Month.” A new problem concerning reliability issues is posted on the site each month, and a solution is proposed. The problem is designed to test and challenge your knowledge of statistical reliability methods.

Additional information on the site includes reliability and life cycle cost, Crow/AMSAA Reliability Growth Plots, and a great article about the cost of unreliability that you may want to copy and place in your boss’s inbox.

Do not miss the paper that states that availability IS NOT equal to reliability except in the fantasy world of no downtime and no failures .

Another great site is http://www.weibullnews.com, the official web site of Dr. Bob Abernethy and Wes Fulton. Abernethy wrote the first Weibull Handbook; Fulton wrote the first widely used Weibull software. They have been developing Weibull PC software for more than 20 years, much longer than anyone else.

The site contains an incredible amount of information about Weibull analysis, created by Walodi Weibull, a renowned Swedish engineer. Be sure and visit “The Weibull World from A to Z” for more information about Monte Carlo Simulation and Confidence, a special technique for simulation made possible with fast computers. It is used as a prediction tool and can provide a reference for analytical techniques. You can also learn more about Weibull analysis. Weibull has the special capability to diagnose failure types such as infant mortality (particularly for electronics), age-independent (accidents and natural occurrences), or wear-out type mechanisms (bearings, filters, etc.).

Join the ranks of professional pilots who use information and numbers to navigate accurately and use statistical reliability methods and tools to land your maintenance department on a world class runway.
Both of these sites and more are on the Reliabilityweb.com Top 100 list of maintenance and reliability web sites. MT

Maintenance experts agree that excessive heat causes rapid deterioration of motor winding insulation. The common rule states that insulation life is cut in half for every 10 C of additional heat to the windings. As an example, if a motor that would normally last 20 years in regular service is running 40 C above rated temperature, the motor would have a life of about 1 year.

Leading standardization organizations have concluded that 30 percent of motor failures are attributed to insulation failure and 60 percent of these are caused by overheating. Articles have been published stating that a significant cause of bearing deterioration is overheating.

There are typically five main reasons for overheating—overload, poor power condition, high effective service factor, frequent stops and starts, and environmental reasons.

Overload conditions
Stator current is frequently used to measure load level, but load level can easily be masked by an overvoltage condition. A common mistake is made in operating at an overvoltage to reduce the stator current and to reduce the introduction of heat. It has been shown that for motors ranging from 10-200 hp, operating at a 10 percent overvoltage would typically decrease losses by only 1-3 percent.

Even though the motor current may vary when applying overvoltages, the excessive damaging heat in the motor will not improve. A load error of more than 10 percent can be introduced by relying on stator current readings to access probable load and heat levels. Under full load conditions, this is the difference between life and death to a motor.

For example at a coal-fired power plant in the United States, a 7000 hp 6.6kV motor was running with only 7 percent overcurrent, but an 8 percent overvoltage. Two identical applications had undergone unscheduled outages in the previous 12 months. A mild overload was identified by examining the stator current of this motor. However, after looking at the true load to the motor, an overload of nearly 20 percent was discovered. This explains why these motors were failing. The repair for each of these three motors ran into the hundreds of thousands of dollars.

In industrial applications, perfect voltage conditions are rare. Losses, not current levels alone, are the true source of heat. These losses are a destructive factor to windings and a significant reason for bearing damage.

This justifies the need for accurate knowledge of operating load level. Only accurate load level calculations can give reliable measurements of excessive losses and overheating in the motor.

Power condition
Electric motors in manufacturing plants generally need to be derated because of poor power conditions in order to maximize their useful life. NEMA MG-1 Sections II and IV specify what voltage quality, as a function of balance and distortion, allows what level of percentage load. Fig. 1 shows the NEMA derating curve for percentage of unbalance. According to the derating curve, the higher the level of unbalance, the lower the acceptable level of steady state load. For example, if a 100 hp motor has an unbalance factor of 3 percent, the motor should be derated to 0.88 or 88 percent of capacity, 88 hp.

The frequent use of variable frequency drives (VFDs) can result in detrimental effects to electric motors because of the condition of power in manufacturing facilities. Fig. 2 shows the voltage that a VFD, running at almost a 6-pulse mode, will send to the motor. The distorted currents are the motor’s reaction to poor power condition. Severe distortions are evident. This scenario shows a NEMA derating of 0.7 which allows the motor to be operated at only 70 percent output.

Effective service factor
T he key to finding the most frequent causes of overheating is accuracy in estimating load level. This can be identified by looking at only currents and voltages. The formula for calculating effective service factor is:

Effective service factor provides predictive maintenance professionals a solid conclusion of stress on any particular motor load application.

In another example, data gathered using a dynamometer showed a 300 hp motor under test was running a nearly full load, 99.7 percent. Voltage distortion was poor due to a previously unidentified silicon controller rectifier defect in the power supply. The resulting NEMA derating factor of 0.85 results in an effective service factor of 1.17, which signaled an alarm condition.

Regardless of nameplate service factor, any motor operating above 1.0 service factor is under stress. A higher service factor signifies the motor’s capability for overload for short periods of time, not higher steady state operating capabilities. Poor voltage conditions are frequent and can be caused by a variety of reasons. NEMA specifies which load level is permitted for poor voltage conditions. On-line monitoring tools capable of accurately calculating operating load ensure plant operation within appropriate limits.

Frequent starts and stops
Table 1 displays the maximum number of starts and stops for line-operated motors as a function of their rating and speed. Limiting the frequency of startup, the most stressful portion of motor operation, is highly important.

Many well-documented cases of recurring motor failure were addressed by increasing the horsepower rating of the motor which shortened the time between failures. However, the root cause of the failure was actually the frequency of starts and stops. The key is to closely monitor the number of starts—hourly for small or medium motors and daily for larger motors.

On-line testing can ensure full compliance to professional standards. It can be used in identifying reasons for failure in operations that do not comply with standards by including these standards in long-term unsupervised monitoring operations.

Environmental conditions
Thermography is frequently used to determine the conditions where electric motors are being used. Poor cooling due to high ambient temperature, clogged ducts, etc., are typical examples of nonelectrically induced temperature stress on both the motor and insulation system. Chemical abrasive substances in the air, wet operation, and high altitude operation are a few common environmental stresses.

Test to standards
Bearing and winding failures are the most common motor failures. The fundamental reason usually is excessive heat. Preventive maintenance practices frequently limit on-line electrical measurements to interpreting current levels. While important, this method is inconclusive in identifying failures caused by excessive winding heat. The best way to ensure successful preventive maintenance and monitoring is to test according to NEMA and other professional standards. Automated assessment is necessary to effectively ensure motor health. MT

Ernesto J. Wiedenbrug, Ph.D., is an R&D engineer at Baker Instrument Co., 4812 McMurry Ave., Fort Collins, CO 80525; telephone (970) 282-1200.

Fig. 1. NEMA derating curve. This figure is also defined in the formula.

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Fig. 2. Extreme distortion with a slow switching VFD (50 hp, 4-pole)

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Table 1. Maximum number of starts and stops for line-operated motors as a function of their rating and speed.

A = Maximum number of starts/hr
C = Minimum rest or off time in seconds between starts

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Enlightenment occurred when, after discussing the details of a tough problem with a plant’s vibration specialists and maintenance engineers, I was brought to the machine site. Two mechanics were waiting, tool boxes ready. The engineer led me to the machine, but for some reason before I took readings I asked to be introduced to the mechanics.

The mechanics looked surprised as I said, “I could solve this problem by myself; however, if I use your brains and knowledge, as well as my own, I could solve the problem a lot faster and most likely with the most accurate answer.”

Their interest heightened dramatically as I indicated I would examine the machine with them, take vibration data, and as soon as I had something to show, we would all go to a nearby office to discuss the findings. The purpose was to use the observations to stimulate their thoughts.

At that point, one mechanic said, “If I were analyzing this pump I’d look at its pipe way up there, near the ceiling.” Why? “I’ve worked in this department for 2 years and about every 2 months I see somebody welding the crack, always at the same place.” Before any further analysis I knew that a resonant pipe with its antinodes and nodes not only caused cracking at the node, but also distorted the spectral data and phase data.

It didn’t take too long before my training courses included mechanics as well as more technically oriented specialists.

One large paper mill had mechanics in each department trained to use instruments and perform most analysis upon startup of machines for which they did the majority of work. This developed their analysis skills enough so they could work with the staff level analyst. When mechanics were confused about their vibration data, they freely asked other mechanics to help. When necessary, they called in the staff specialists. Conversations with the staff people were on a relatively equal basis, and not a specialist with all the knowledge talking to a mechanic who is ignorant on the subject.

After about two years the top staff analyst called to tell me how much better his job was. “We finally realized that we used to treat the mechanics as if we specialists had all the brain power and they were just an extension of the wrench. We found that the mechanics not only have good brains, but they really want to use their brains. As we showed we valued their input they actually worked harder to improve the machines they worked.” It is said that people who do mediocre work, feel mediocre about themselves; people who do good work—very good work—feel very good about themselves.

This was really brought home to me in a discussion with the former quality control manager at Rolls-Royce. I told him my own guesses as to why a Rolls-Royce turns out so much better than an ordinary “good” car. I surmised that Rolls simply learned to accomplish each operation with so much more precision. He chuckled and said, “That’s not really how we do it.

“We spent much more effort and sessions creating a sense of pride and expectancy than we did on actual techniques for skills.” His lesson reminded me of so many plants that were getting the best work for the smoothest running machines when the mechanics knew that management expectancy for precision and care was very high. They enjoyed providing what was expected.

This good work needs to be recognized. One large power company in one of its monthly machinery improvement and troubleshooting newsletters reported, in short articles, the good work of the mechanics that improved a machine’s performance. The articles had diagrams and congratulations to the mechanic from the various managers involved.

Yes, good vibration analysis and correction does require good instruments, good training, good engineers, good supervisors, good analysts, and other specialists. But the catalyst to make it all work easier, faster, and more knowledgeable is when the so-called “ordinary mechanic” is not treated like an “extension of the wrench” but instead, is a thinking person with good knowledge about the machine, who enjoys doing good work, and is acknowledged by supervisors, managers, other mechanics, and maybe even his mother. MT

Ralph T. Buscarello, CEO of Update International Inc., Denver, CO, has conducted vibration-related machinery improvement seminars in more than 45 countries.

Robert C. Baldwin, CMRP, Editor

I believe that mantra works within the maintenance organization as well. Where would you rather focus your physical asset management time and resources? On activities that improve asset reliability and offer significant payback or on lesser supportive activities?

The answer is obvious, but there is a lot of work to do in getting there. The first step is to define your core competencies.

What might those core competencies be? They are often hard to identify because your comfort and skill level with each often color your judgment. And it makes a difference of where you stand in the competency continuum. The more competent you are, the better equipped you are to see the difference between core and noncore activities.

To make those difficult core-noncore judgment calls, the effective maintenance and reliability manager needs personal competency in several sectors. The SMRP Certifying Organization has identified five: business and management, process reliability, equipment reliability, people skills, and work management. Each of those interrelated core work practices has three elements: Strategy development and planning, implementation and measurement of results, and review and analysis of results and continuous improvement. It is likely that you will have to call on all of them before you can identify your core departmental competencies with any level of confidence.

When you take a hard look at what your organization is doing, you will undoubtedly find a significant portion of that work does not materially affect equipment reliability. Those are the activities to be transferred or eliminated.

But getting rid of them can be difficult. We are often quite good at some noncore competencies and are emotionally attached to them, making it hard to cut them loose.

And that’s the trick—becoming hard core in your approach to maintenance and reliability. It is the difference between efficient maintenance and effective maintenance.

Hard-core maintenance is more than just doing things right. It is the commitment to doing only the right things and eliminating or outsourcing the rest. MT

Over the past several years, managers up through the CEO have come to recognize equipment uptime as a key part of any successful operating strategy. Equipment availability is one of the key performance indicators of a maintenance organization. Goals are set based on “gut feel,” or by benchmarking with similar facilities within the organization or with similar organizations within the same industry.

Both these goal-setting methods involve high levels of uncertainty that can lead to overspending for maintenance and overtaxing of maintenance resources. The uncertainty of “gut feel” speaks for itself. Benchmarking involves high levels of uncertainty due to the difficulties created by not knowing the exact guidelines each facility uses for recording unavailability.

Here is a framework for managing availability goals to help meet the financial goals of an organization. We will examine availability in detail: the three types of availability and how they relate to each other, the factors that determine availability, and recommendations for improving the setting of goals.

Availability types
The three subtypes of availability are inherent, achievable, and operational (see Fig. 1). Each subtype has specific characteristics determined by:

• Inherent availability (A_i): The expected level of availability for the performance of corrective maintenance only. Inherent availability is determined purely by the design of the equipment. It assumes that spare parts and manpower are 100 percent available with no delays.
• Achievable availability (A_a): The expected level of availability for the performance of corrective and preventive maintenance. Achievable availability is determined by the hard design of the equipment and the facility. A_a also assumes that spare parts and manpower are 100 percent available with no delays.
• Operational availability (A_o): The bottom line of availability. It is the actual level of availability realized in the day-to-day operation of the facility. It reflects plant maintenance resource levels and organizational effectiveness.

It is important to understand the distinctions among the three subtypes in order to design, measure, and manage integrated subgoals:

• Achievable availability fulfills the need to distinguish availability when planned shutdowns are included.
• Inherent availability fulfills the need to distinguish expected performance between planned shutdowns.
• Operational availability is required to isolate the effectiveness and efficiency of maintenance operations.
• These definitions and distinctions lead to crucial recognitions:
• The shape and location of the achievable availability curve is determined by the plant’s hard design.
• An operation is at a given point on A_a, based on whether scheduled or unscheduled maintenance strategies are selected for each failure. A goal of availability-based maintenance operations is to find the peak of the curve and operate at that level.
• Operational availability is the bottom line of performance. It is the performance experienced as the plant operates at a given production level.
• The vertical location of the A_o is controlled by decisions for resource levels and the organizational effectiveness of maintenance operations. By definition, its location cannot rise above A_a.

These factors have the following strategic implications:

• It is crucial to know the location and shape of the achievable availability curve. Otherwise, it is not possible to determine what is reasonable and possible for operational availability and, therefore, plant production.
• If the A_a curve is not known, manufacturing operations management may unknowingly attempt to achieve performance beyond that which is possible. The result is the overspending and overtaxing of maintenance resources.
• Management must make strategic decisions for long-term relative positions of the two curves. As plant production increases over time, changing operating conditions will place greater stress on equipment and drive A_a down. Meanwhile, maintenance operation management will progressively move A_o upward to meet the demands of production. Eventually the two will converge to the point that additional availability can be acquired only by modifying plant design.

The conclusion from these factors is that eventually A_a must be known. Otherwise, many of the current goals to develop world-class maintenance operations are not possible. It is the organization that makes the most money—not the one with the highest availability—that wins the game.

Determining availability
Availability is a function of reliability and maintainability—in other words, how often equipment will fail and how long it takes to get the equipment back to full production capability. Reliability, maintainability, and therefore, availability, are determined by the interaction of the design, production, and maintenance functions (see accompanying section “Top- Level Factors That Affect Availability”).

The implication is that availability is largely determined by how well designers, operators, and maintainers work together.

Optimizing availability
Profitable plant availability is the result of optimizing A_i, A_a, and A_o. Because no plant can achieve availability higher than A_a, achievable availability is the first to be optimized (see Fig. 2).

All equipment fails based on its design even when operated and maintained perfectly. Every maintenance activity, whether scheduled or unscheduled, is representative of an equipment failure. Scheduled or time-based maintenance seeks to correct failures before they can affect equipment performance. Unscheduled maintenance is corrective maintenance performed as the result of breakdown or the detection of incipient failure.

Achievable availability is the result of several factors:

• Plant hard design determines the shape and location of the A_a curve. Therefore, this design establishes the possible achievable availability.
• Maintenance strategies determine the plant’s location on the A_a curve. Therefore, these strategies establish the actual achieved availability.
• The right extreme of the A_a curve represents the hypothetical extreme of 100 percent scheduled maintenance. There are no surprises because all maintenance is performed during a scheduled maintenance period. Availability is well below optimum. This extreme can be compared to coming into the pits during every lap of a race to ensure that you have no breakdowns on the racecourse. It could be done, but you would never win the race.
• Trading off scheduled maintenance for unscheduled maintenance results in a climb back up the availability curve to the left. A nearly linear increase in availability occurs until you reach the point where unscheduled maintenance due to breakdowns takes away from availability gains. Operating farther to the left places the equipment under more stress and increases organizational chaos.
• After reaching the left of the peak A_a, further reductions in scheduled maintenance become poor strategies.

The cost curve represents strategic decisions to invest large amounts of capital up front to increase A_a through hard design, or to spend operating dollars to increase A_a through more intensive maintenance strategies. These decisions are driven by many factors, such as the need to get a product to market quickly, the availability of capital, and the operating mentality of the company.

Availability and costs
The availability/cost curve relationship highlights the fact that availability is a proxy of revenues. At some point of either extreme of the cost curve or the availability curve, the cost of availability will exceed the income it allows. Without availability management, operating beyond those intersections can occur without management’s awareness; normal accounting practices and other maintenance performance indicators cannot easily reveal this practice.

The difference between achievable and operational availability is the inclusion of maintenance support. Achievable availability assumes that resources are 100 percent available and no administrative delays occur in their application. Therefore, maximum operational availability theoretically goes to achievable availability. In reality, every human endeavor has a natural upper limit of obtainable perfection that prevents A_o from reaching A_a.

The shape and location of the operational availability curve are determined by the level of maintenance operation resources and organizational effectiveness. Resources and organizational effectiveness have upper bounds above which additional spending will not yield better results. At that point, achievable availability must be increased to give A_o room to move upward. A_a can be increased by new maintenance strategies, provided that the plant is not operating at the peak of the A_a curve. Capital investment is required to move the A_a curve upward if the plant is operating on the peak.

This is important. Without availability engineering and management, it is easy to unknowingly spend beyond the point of maximum return. This may occur when plant performance falls short of management’s desired productive capacity. Management tries to achieve gains with increased stress on maintenance support. However, the operational availability curve has already been unknowingly forced against the achievable availability curve. The result is throwing good money after bad. Spending is in the loss zone to the right of the intersection of the achievable availability and cost curves.

Determining achievable availability for an existing facility
Few physical asset managers have had the luxury of being an integral part of the design phase of their physical plant. Therefore, they need to analyze the current physical plant to determine its achievable availability.

Determining achievable availability is a four-step process:
1. Build a reliability block diagram (RBD) of the plant’s critical systems. Use publicly available reliability data for failures. Using plant data skews the results based on plant organizational effectiveness. Use plant data or works estimation techniques to determine mean time to repair. Again, using plant data skews the results based on plant organizational effectiveness.

2. Determine logistical delays created by plant hard design: to/from shops, to/from stores, accessing equipment.

3. Add in scheduled maintenance downtime for the chosen preventive maintenance strategy.

4. Perform availability simulations.

The scope of the analysis is determined by resources, time, and the desired quality of the result.

Building the RBD
The RBD is a graphical representation of the plant systems, subsystems, and components arranged in a way that reflects equipment interdependence (see Fig. 3). The RBD is the cornerstone of the availability model because it shows how failure in a plant element affects process uptime.

It is important to note the reliability implications of the systems presented in Fig. 3. Serial systems are inherently unreliable. The failure of a single element in the system results in a stoppage of the overall system. Fully redundant parallel systems are inherently reliable. The system stops only if all the redundant systems fail at the same time. Redundancy is an important tool in improving overall system reliability. See Practical Machinery Management for Process Plants, Volume 1, 3rd Edition: Improving Machinery Reliability by Heinz P. Bloch for a more complete discussion.

All complex machines are built from the same few basic machine elements of couplings, bearings, gears, motors, belts, and so on. The RBD is refined by breaking down the top-level RBD into several RBDs that represent each top-level system (see Fig. 4).

Obtaining failure and repair data
After the RBDs are built, failure and repair data must be obtained for use in availability simulations. Obtaining this data is a time-consuming task. The desired degree of certainty dictates the level of effort required for this stage of building the model. It is important to remember that this is not an exact science. Perfection is not required. You need only be better than your toughest competition

There are many sources for failure data. This is not an exhaustive list:

Reliability Analysis Center—Electronic parts reliability data (EPRD), non-electronic parts reliability data (NPRD). Available in print and software versions.

Paul Barringer’s Web site—Weibull data for many components plus links to other available data and reliability web sites.

Practical Machinery Management for Process Plants, Volume 1, 3rd Edition: Improving Machinery Reliability, Heinz P. Bloch, Gulf Professional Publishing, ISBN: 087201455X—Table of equipment failure data plus practical information on improving equipment and system reliability.

Plant data—Failure data depends on the robustness of the data-collection system. Using plant data skews the analysis by including plant organizational effectiveness.

Binomial and Weibull distributions typically are used to present failure data for modeling purposes. Most availability simulators accept either type of data.

Obtaining repair data is a much more difficult task. Repair data is typically not available anywhere in tabular form. Repair times are very dependent on the configuration of the equipment and the plant. Equipment with a great deal of guarding and with parts located in tight spots requires much longer repair times than equipment with little guarding and plenty of space in which to work. The two primary methods of obtaining repair time data are analyzing current plant data and using works estimation systems such as MOST to estimate times (see accompanying section “Obtaining Repair Time Data”). Each method has its own set of difficulties.

In the next installment of this article, we will discuss using the availability model to determine plant bottlenecks and increase throughput, the impact of the need for modeling and analysis on the maintenance and engineering organization, and offer suggestions on how to close the natural gaps between the three types of availability. MT

Bill Keeter is president of ARMS Reliability Engineers-USA, LLC, 8450 N. Devonshire Woods Pl., West Terre Haute, IN 47885; (812) 535-1445

THREE TYPES OF AVAILABILITY

Fig. 1. It is important to understand the distinctions among the three subtypes in order to design, measure, and manage integrated subgoals.

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OPTIMAL AVAILABILITY/COST

Fig. 2. Because no plant can achieve availability higher than achievable availability, A_a, it is the first to be optimized.

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RELIABILITY BLOCK DIAGRAMS

Fig. 3. The RBD is the cornerstone of the availability model because it shows how failure in a plant element affects process uptime.

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BREAKING DOWN RBDs

Fig. 4. Breaking down the top-level RBD shows several RBDs that represent each top-level system.
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TOP-LEVEL FACTORS THAT AFFECT AVAILABILITY

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OBTAINING REPAIR TIME DATA

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Knowledge, as I use it here, is defined as the capability for understanding and being able to use information and processes. As mill manager I knew, based on full run capacity, that our output could be increased substantially; holding us back was the combined effect of a multitude of relatively minor (individually) problem areas that produced frequent production stoppages.
In less than one year, the KFP mill, through work process improvements only, increased output to more than 80 million board ft/yr. How was a stud mill able to increase production by 54 percent without capital equipment or plant expansion? Through a complete cultural renaissance within the mill’s workforce.

Pre-renaissance
The KFP workforce possessed an embedded, almost instinctive, knowledge of the mill’s established routines and processes. Within the maintenance organization these processes were basically reactive. The plant culture, its mindset gained through long-term practices, was to react to failures, fix broken equipment, and, in general, respond to production slowdowns and stoppages.

Our “repair-focused” culture was typified by attitudes that production runs it until it breaks and the maintenance crew is simply responsible for fixing the problem, without looking at its cause. This approach led to repetitive fixing of symptoms rather than resolving the problem causes. The general condition of our equipment was steadily deteriorating.

We did not have a computerized maintenance management system (CMMS) and the storeroom was snarled with a multitude of parts being ordered daily for jobs to be completed in the current week or even the current day. The parts that were in stock were not uniformly identified or systematically stored.

Solving such a multitude of smaller problems, which had created this repair-focused culture, was a question of finding a solution that addressed as many of the problem areas as possible. Our renaissance began in that first spring of the new century when a wellspring of change was created at KFP.

During the search for an integrated solution, a member of the mill staff attended a seminar entitled “Maintenance Excellence” presented by Life Cycle Engineering, Inc. (LCE), North Charleston, S.C., a company specializing in maintenance engineering. Its seminar addressed the essential elements for initiating transition to a world class maintenance operation. It also addressed the dramatic changes in equipment reliability, production, and profitability that could be expected from achieving maintenance excellence.

The employee’s enthusiasm, combined with the logic of the information, led me to conclude that the Maintenance Excellence philosophy must be applied to KFP’s maintenance operation and to the overall cultural mind set of the mill’s workforce. That day, we set out to reshape the mill in the form of the Maintenance Excellence model (see Fig. 1).

The path to cultural renaissance
The process for change began with a maintenance assessment to:

• Identify and prioritize the maintenance process problem areas
• Define the solutions and goals of changed processes
• Establish a base line of the maintenance effectiveness of the existing organization so that progress toward achievement of maintenance excellence could be accurately gauged.

In order to conduct an unbiased, objective evaluation, we sought an outside contractor to perform the evaluation of our maintenance operation as well as to provide support services and technical and management guidance to the mill for reconfiguring for maintenance excellence. LCE, the maintenance engineering firm that had presented the seminar, was selected. The company provided trained specialists to perform a comprehensive and structured maintenance assessment. Following the assessment, they performed an analysis of the gap between existing work processes and the best maintenance practices of maintenance excellence. The purpose of the analysis was to identify and prioritize the areas where changes were required.

Based on the maintenance assessment report and analysis, a master plan of action (MPOA) was developed to organize for and apply the Maintenance Excellence model within the mill. Major action items in the plan included:

• Selection and implementation of a functional CMMS
• Performance of equipment condition upgrade and restoration activities on critical, failure-prone equipment
• Identification of key maintenance effectiveness metrics (what data to collect, analyze, and track that could measure—and quantify—the impact of process changes on the effectiveness of maintenance activities)
• Development of equipment maintenance plans (EMP) to provide the foundation of a formal planned preventive maintenance (PM) program
• Development of bills of material to serve as the basis for determining storeroom stocking parameters
• Creation and establishment of the maintenance planning and scheduling function.

In order to successfully execute the MPOA, our next step was to develop a set of governing principles and operating practices that would define the mill’s goals and objectives, organizational strategies, and operating guidelines. The principles developed were then agreed upon by all mill management, union, maintenance, and operating personnel. These new principles, the defining factors of the new culture, were documented, signed by all participants, and prominently posted within the mill. This document has served as a reminder for all on how business would be conducted from that day on.

Next, applicable parameters and measurement/tracking methodologies (performance metrics) were identified to monitor, measure, and track the progress toward achieving maintenance excellence.

The pursuit of several of the major action items was facilitated through the creation of focus teams, staffed by both operations and maintenance personnel and provided with designated team leaders, to develop the details of individual action plans. The objective of the focus teams was to move promptly into implementation and execution as soon as the detailed action plans were approved.

The renaissance
One focus team was chartered to select and implement a CMMS. It was provided coaching and technical expertise from LCE. Through the use of a proven CMMS vendor selection process, three systems were identified and evaluated. Based on responses, budget, and vendor demonstrations, Ann Arbor, MI,-based CK Systems’ MaintiMizer 2000 was selected and implementation activities were initiated. A detailed standard operating procedure (SOP) was developed to ensure all process and utilization decisions were documented and standardized. The SOP would later become KFP’s “Maintenance Bible.”

A reliability focus team was chartered to address equipment reliability issues, which included evaluating and, where necessary, upgrading equipment condition and performing general restoration activities. The team also developed the EMP, making use of the current knowledge level and conditions observed during the equipment reliability evaluations and condition upgrades. The EMP would be the basis for development of the mill’s planned PM program. The reliability focus team’s activities accomplished a number of positive results:

• Identified the repairs, modifications, and upgrades required to restore the mill’s equipment to optimum operating condition
• Built a backlog of maintenance that would be required for proper planning and scheduling
• Very quickly began to influence operations through steadily increasing production output.

LCE again provided expertise to work with our maintenance staff to assist, coach, and mentor team members during these activities to ensure effective maintenance techniques were utilized.

A maintenance planner was selected from the existing team, and he was provided with extensive planner/scheduler training and follow-up in-mill coaching from LCE. Among the planner’s first responsibilities was the development of an equipment hierarchy (identification, parent-child and ownership relationships, standardized nomenclature, redundancy and commonality, etc.) for the entire plant. The equipment hierarchy provided the basis for tracking and relating labor, parts and material, and other costs to systems and equipment, down to the component level, as well as cataloging equipment history for each item in the mill.

We also decided to acquire a material management specialist to work with the planner, plant maintenance, and purchasing personnel to establish a functional storeroom. This allowed parts, materials, and consumables to be provided for maintenance tasks on a pre-planned basis and to establish more effective cost control measures. Almost immediately, this action resulted in a significant improvement in parts availability. Total cost of inventory was reduced dramatically and costs for emergency parts procurements were nearly eliminated. Later, the implementation of bar coding, integrated into the CMMS, further enhanced the efficiency of storeroom operations.

I felt that one final action item was needed to thoroughly imprint the change of culture within the mill. We instituted mill-wide training on the newly established workflow and all new work processes as well as CMMS operation and utilization, root cause failure analysis, storeroom procedures, and, through utilization of the metrics of maintenance effectiveness, the constant improvement process. This served not only to educate, but also to emphasize the importance of every employee in the mill for the success of the cultural change.

The renaissance completed
Within a few months of implementing these initiatives, the measures of maintenance effectiveness were visibly showing us that, through the performance of planned maintenance, more work was being accomplished and equipment reliability was improving steadily. Even more significant were the increase in production and the resulting climb in total sales revenues. With improved maintenance, the mill was able to start a third operating shift over the weekend. The combined effects boosted annual volume by 54 percent to 80 million board ft and reduced the operating cost per board foot produced dramatically.

Within 2 years of adopting the maintenance excellence culture at KFP, the results were more dramatic. The return on investment of the cost of implementation was nearly 10-fold. Today, I am convinced that, had Kenora Forest Products not embraced the tenets of maintenance excellence, the mill would not have survived the volatility of the lumber market and the increasing burden of tariffs imposed upon the company. MT

Information supplied by Tom Dabbs, Life Cycle Engineering, Inc., North Charleston, S.C.; (843) 744-7110 ext. 220.

ELEMENTS OF MAINTENANCE EXCELLENCE

Fig. 1. Using this model enabled the mill to increase production by 54 percent without capital equipment outlays or plant expansion.

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The previous article in this series, “Determining Accurate Alignment Targets” (MT 2/03, pg. 45), presented an example of thermal growth and its affect on equipment alignment at a wastewater treatment plant in Cleveland that needed realistic cold alignment targets for a 3600 rpm compressor. Another example is a project that involved performing off-line-to-running examinations on two identical machines at a cogeneration facility in Virginia.

The machines are gas turbine generator units that experienced high vibration issues at particular times along their operating cycles. These units were considered identical in terms of manufacturer, size, containment structure, load rating, installation, rpm, etc. A laser-based monitoring system was set up on both units and the setup dimensions were programmed into the computers. Data collection was started and the machines were placed into their startup modes at approximately the same time.

Dramatic difference seen
While the trended changes in the alignment had the same basic shape to the graph, one of the units showed a dramatically different change in the vertical offset alignment. Both machines are supposed to operate at the same temperature and both machines were set to the OEM-recommended cold alignment targets.

Unit No. 5 showed approximately a +20 mil maximum change in the vertical offset and settled around +10 mils at normal operating conditions.

Unit No. 6 showed approximately a +30 mil maximum change in the vertical offset and settled around +20 mils at normal operating conditions.

The OEM technical documentation states that the generator will grow 20 mils evenly front to back and the clutch will grow 22 mils evenly front to back. That results in a +2 mil change in the alignment from off-line-to-running at normal operating conditions. As noted, this value is not accurate and does not reflect the actual operating condition of either machine.

Compared to the recommended tolerances for the 3600 rpm machine, ±2 mils vertical offset misalignment, Unit No. 5 is operating with a vertical offset of +8 mils and Unit No. 6 is operating with a vertical offset of +18 mils. These particular machines have been operating under these conditions since their installation more than a year prior and have a history of high vibration readings and premature clutch failures since their first day of operation. The test on both units required less than one day to complete.

Consider dynamic movements
The cost of a precision alignment is typically small when compared with the loss of production should a critical piece of equipment fail. Even with the introduction of portable vibration monitoring equipment and easy-to-use laser alignment systems, alignment still ranks as one of the leading contributors to premature rotating machinery failure and lost production. One reason is the neglect or miscalculation of machinery dynamic movements. It has been shown that besides cold alignments, the actual dynamic movements of machinery need to be considered when aligning.

The problem of ignoring dynamic changes in the shaft alignment of two machines from off-line-to-running condition needs more attention. There is mounting evidence that long-standing assumptions are leading to machine reliability problems—assumptions such as believing identical machines have identical dynamic movements, relying solely on OEM recommendations, ignoring the possibility of horizontal movement, assuming growth will be symmetrical, and accounting only for thermal effects. These assumptions need to be challenged and behaviors changed.

The options available on the market today until very recently have not been enticing. Optical methods, mechanical methods, and laser-based monitoring systems all require some special skills and expertise to obtain good results. It may be prudent to contract these services for critical equipment rather than attempting to develop the skills in-house since the learning curves can be steep. A Swedish manufacturer has introduced a device that greatly facilitates in-house measurement of machinery dynamic movement.

Regardless of the approach, coupled machines need to be set to cold alignment targets that will reflect the actual changes in the shaft alignment. This will lead to lower vibration levels, increased mean time between failures, decreased maintenance expenditures, and increased production. Much like the philosophical change from aligning shafts with dial indicators to aligning shafts with laser-based systems, these types of measurements will take some time to be generally accepted and routinely practiced. While some of the current technology may be relatively expensive, a simple cost/benefit analysis will help with the right decision, which can yield a significant increase in machine availability and profit. MT

Contributors to this article include Rich Henry, Ron Sullivan, John Walden, and Dave Zdrojewski, all of VibrAlign, Inc., 530G Southlake Blvd., Richmond, VA 23236; (804) 379-2250; e-mail info@vibralign.com