http://www.reliableplant.com/article.aspx?articleid=20549

:-)

Göran Wikingson, CBM-Sweden

FR2000 Verksamhetsledning

Toyota presentation

http://www.fr2000forum.se/doc/BD.pdf

http://www.fr2000forum.se/

http://www.fr2000.se/

http://books.google.se/books?id=zZkOWCJ755oC&pg=PA54&lpg=PA54&dq=%22Fact+Based+Maintenance%22&source=bl&ots=7qxk9zp7Xh&sig=3MorIsE0MJ59Xhd2NwSRLy5A_co&hl=sv&ei=25fFSvGBBtPP-QaTtJk-&sa=X&oi=book_result&ct=result&resnum=1#v=onepage&q=%22Fact%20Based%20Maintenance%22&f=false

BOOK:

Maintenance Decision Making

Liliane Pintelon, Frank Van Puyvelde

Innehåll

http://www.dailynews.lk/2009/08/21/bus30.asp

Total Productive Maintenance

A new paradigm in manufacturing:

Thilak Pushpakumara (JIPM accredited TPM instructor) CEO/Lean Management Consultant; Institute of Lean Management (Pvt) Ltd Former Plant Manager, Unilever, Sri Lanka, Former General Manager, Productivity Improvement and Training and Development, Loadstar Pvt, Limited

Total Productive Maintenance (TPM) is a unique Japanese system which has been developed from the Preventive or Productive Maintenance (PM) concepts introduced from the USA. Preventive Maintenance was introduced in 1951 and then it developed into Corrective Maintenance in 1957. Maintenance Prevention was introduced as an activity to re-design the equipment and line, in order to be maintenance free.

The Japan Institute of Plant Maintenance (JIPM) began promoting TPM in fabrication and assembly industries and later it was actively adopted in process industries. In 1971, Nippon Denso Co. Ltd. First introduced and successfully implemented TPM in Japan. This was the beginning of TPM in Japan. Since then, TPM has spread throughout Japan, especially in the Toyota group. However, TPM has made a gradual change and the tendency to implement Condition Based Maintenance (CBM) can be seen from the early 80s. Implementing TPM in administrative and support department is increasing rapidly in Japan.

Interest in TPM outside Japan has also expanded in recent years. Many companies in the United States, Europe, Asia and South America are planning to or are actively pursuing TPM. Many companies in India have implemented TPM and achieved both tangible and intangible results. Unilever Sri Lanka and Premium Exports - Agarapathana (a subsidiary of Unilever) have implemented TPM and they received the TPM Excellence award from JIPM (Japan Institute of Plant Maintenance) in 2007.

Why is TPM so popular? Because it guarantees dramatic results, visibly transforms the work place and raises the level of knowledge and skill in production and maintenance workers. TPM helps restructure the corporate culture through improvement of human resources and plant equipment.

The production operator will get the ability to perform "Jishu Hozen" (Autonomous Maintenance) and he will transform into a maintenance technician. The maintenance technician will carry out only high quality, complex maintenance tasks and learn about executing a maintenance free equipment plan. In other words, maintenance engineers would transform into design engineers.

A unique feature of TPM is "Jishu Hozen" performed by operators and small group improvements through Kobetsu Kaizen activity (Focused improvement). TPM small group activities are an integral part of the formal activities of the organization. TPM small groups encompass the whole of the organizational hierarchy, from top management through middle management to the front line. TPM combines top down management - by objectives with bottom-up-front line, small group activities. The mechanism of TPM promotion is based on this philosophy. The success of small group activities depends on three factors, viz. motivation, ability and opportunity. While motivation and ability are matters of individual concern, opportunity is a question of environment. Satisfying all three requirements is an essential task for managers and supervisors in their roles as leaders.

The prevention philosophy in human beings and practising "gemba - gembutsu" with automation will build up a profitable corporate culture. Visuals and visual controls at the workplace and the kaizen philosophy (continuous improvement culture) creates a work friendly environment here people can work happily.

The attitude transformation from "I run you maintain," to "I run I maintain" is a key achievement in TPM through Jishu Hozen.

TPM aims at establishing a corporate culture that will maximize production system effectiveness, organizing a "gemba-gembutsu" (the concept of "go and see the actual thing"), system to prevent losses and achieve such reduction to zero targets as "zero accidents", "zero defects", and "zero break downs" in the entire production system life-cycle, involving all functions of an organization including production, development, sales and management involving every member of an organization from top management to front line operators and achieving zero losses through the activities of overlapping small groups.

Even though TPM was defined as Total Productive Maintenance, recently "P" came to connote "Perfect, Production or Profit", while "M" includes "management" besides maintenance.

5S is the foundation of a World - Class organization and no improvement method, concept can succeed without the basis of organization and standardization provided by the 5S's. 5S creates a safe, pleasant and work friendly environment and everybody loves it and increases employee's morale. Once 5S progress is at a steady pace, we can easily get the people's participation for the TPM journey. Once we establish a sustainable 5S organization with disciplined people, with disciplined thoughts and disciplined actions we can introduce any concept. That is why TPM starts with 5S.

TPM aims to establish good maintenance practices through the gradual pursuit of "the eight pillars of TPM" that would cover the entire organization including the supply chain, sales and marketing.

The eight pillars of TPM are -

1. Focused Improvement

2. Autonomous Maintenance

3. Planned Maintenance

4. Education and Training

5. Quality Maintenance

6. Early Management

7. TPM in Administration and Support

8. Safety Health and Administration

Focused Improvement is an activity performed by cross-functional project teams composed of people such as production engineers, maintenance personnel and operators. The activities are designed to minimize targeted losses that have been carefully measured and evaluated. Early management addressed both early product management and early equipment management.

http://www.industrysearch.com.au/Products/Maintelligence-tool-for-managing-your-maintenance-management-31380

Maintelligence - tool for managing your maintenance management

http://www.highbeam.com/doc/1G1-208414543.html

Article: Scientists at Fatih University publish research in safety engineering.

According to recent research from Turkey, "Condition Based Maintenance (CBM) aims to balance two extreme sides (i.e., Corrective Maintenance ( CM), and Preventive Maintenance ( PM)) by observing and forecasting the real time health of machines. Recent developments in CBM revealed promising technologies for advanced fault detection, and forecasting."

"Traditional maintenance scheduling in CBM is based on the threshold setting on forecasted failure probability, or remaining useful life (RUL) for individual components. However, this approach may not give the best result for the system, because individual components are inter-related, and mutually dependent. It is ...

http://www.tardec.info/events/CBM07/presentations/MsJackson.pdf

http://www51.honeywell.com/honeywell/news-events/press-releases-details/08.12.09AdditionalYearArmy.html

http://www.missionready.com/pdf/tiger_brochure.pdf

http://www.honeywell.com/sites/aero/technology/aerotechmagazine3_C6CF7D843-6C1D-21B4-CBF6-0D99E3E4E24B_HF935E9FD-5F49-855C-3690-B11CFC11C519.htm

Total InteGrated Engine Revitalization (TIGER) A Total Life Cycle Support Solution for the M1 Abrams AGT1500 Tank Engine

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The Total InteGrated Engine Revitalization (TIGER) Program Year 4 Option has a Not-To-Exceed value of more than $300 million, bringing the total contract value to more than $1.4 billion.

Honeywell is working with the Army's Program Manager Heavy Brigade Combat Team, TACOM Life Cycle Management Command (LCMC) and Anniston Army Depot (ANAD) under the contract to provide parts, engineering, depot and field support services, enabling ANAD to reset and maintain approximately 750 engines. The scope of work includes critical field support services at U.S. military bases and in South Korea and Germany.

In the fourth year of the TIGER program, the Abrams engine is transitioning to a Fact Based Maintenance (FBM) protocol in keeping with the Army's transition to Condition Based Maintenance (CBM). Fact Based Maintenance will be utilized in evaluating and repairing previously reset TIGER engines returned to ANAD for maintenance. As a result of the TIGER Program, baseline configuration, field maintenance and engine performance data will be available on all returned TIGER engines, enabling ANAD personnel to determine specific maintenance requirements during engine teardown analysis. Significant cost-savings for the Army will be realized by transitioning from an "overhaul" protocol to FBM and replacing only parts that do not have adequate life remaining to complete another field cycle.
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Honeywell's Total InteGrated Engine Revitalization (TIGER) program delivers an integrated lifecycle management approach to improve the operational readiness and durability of the M1 Abrams AGT1500 tank engine while reducing operating and support costs.

Teaming with the Program Manager Combat Systems (PM-CS), Tank-automotive and Armaments Command (TACOM) and Anniston Army Depot (ANAD), Honeywell's TIGER program provides a truly integrated solution for supporting the AGT1500 engine. As a comprehensive integrated logistics service program, TIGER delivers Original Equipment Manufacturer (OEM) engineering support, supply chain management, field repair support, Fact Based Maintenance (FBM) data collection systems and logistics.

The TIGER program delivers an integrated lifecycle management approach to improve the operational readiness and durability of the AGT1500 engine while reducing operating and support costs and reducing total ownership costs and mitigate readiness risk for the Abrams fleet.

Through TIGER's comprehensive technical services, depot support, supply chain management, technical field support and service and fact-based maintenance Honeywell creates an integrated, affordable performance package that ensures quality OEM parts, consistent repair and overhaul procedures, and timely logistic services to increase the operational readiness of weapon systems throughout their life cycles.

Key TIGER Program Benefits

	Improved operational readiness and weapon system availability
	Increased engine durability and longer engine depot return intervals
	Reduced operating and sustainment costs
	Improved parts availability
	Field durability data tracking
	Flexible supply chain to effectively manage changing demands
	Established fact-based maintenance protocols
	Increased OEM field service and technical support
	Improved maintenance and support processes to reduce engine variation

http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=1609126&isnumber=33790

Conditioned based maintenance (CBM) Holguin, L.   Aviation & Missile Res., Dev., & Eng. Ctr., Redstone Arsenal, AL; This paper appears in: Autotestcon, 2005. IEEE Publication Date: 26-29 Sept. 2005 On page(s): 188-193 Location: Orlando, FL, ISBN: 0-7803-9101-2 INSPEC Accession Number: 8957994 Digital Object Identifier: 10.1109/AUTEST.2005.1609126 Current Version Published: 2006-03-27

Abstract The Army's Research Development and Engineering Command's, Aviation Missile Research Development and Engineering Center (AMRDEC) is leading a prognostic and diagnostic revolution to incorporate conditioned based maintenance (CBM) into the U.S. Army's rotorary aircraft programs. By combining the CBM concept with existing AMRDEC diagnostic products and in-house created algorithms, opportunities have arisen for methods of prognosis utilizing data from our automatic test equipment (ATE). Implementing diagnostics and prognostics into army weapon systems presents an opportunity to reduce system downtime and costs associated with unscheduled maintenance. By enabling CBM, diagnostics and prognostics can dramatically reduce the need for routine tear-down for inspection, repair or replacement of critical components. Fault detection creates an opportunity for self maintained actions that can autonomously correct emerging faults and perform routine system health functions or maintenance

Business Intelligence can be an enabler for performance improvement across multiple areas of a manufacturing business. In this blog I will focus on Overall Equipment Effectiveness (OEE).

What is Overall Equipment Effectiveness (OEE)?
OEE is a measure of the throughput of acceptable quality units over the scheduled operating time of a piece of equipment, cell, line or plant

Why is improving OEE important?
The motivation is straightforward. If a company can use its total resources better than its competition it will have a lower cost structure, which enables it improve margins and/or better sell commoditized products in competitive markets.

How do you improve OEE?
Measure and monitor how well a piece of equipment performs relative to its designed capacity. Analyze the causes of sub-optimal effectiveness, and implement corrective action.

What can BI enable?
Better visibility and insight into the Key Performance Indicators (KPIs) used to calculate OEE.

(OEE = Availability x Performance x Quality)

Availabilitythe percentage of actual operating time compared to scheduled operating time

Performancethe speed at which the equipment runs as a percentage of its designed/target speed

Qualitythe good units produced as a percentage of the total units started

What can be achieved with better visibility and insight?
Reduced down timeAlthough equipment breakdowns can not be completely eliminated, minimizing unplanned down time is critical to production performance. BI can be used to display data from Computerized Maintenance Management Systems (CMMS) in ways that help production quickly identify trends and variances in breakdowns and easily drill down into the detail that explains the root causes.

Using BI a Consumer Packaged Goods company found the average Mean Time Between Failure (MTBF) on one of their pieces of equipment ranged from 2 to 6 years. Analysis showed that the replacement parts from a particular vendor consistently resulted in 5-6 year MTBF. By standardizing on that vendor for replacement parts the company was able to significantly reduce down time and postpone capital expenditures for additional equipment.

Increased throughputSometimes a piece of equipment does not fail outright but operates at a reduced rate. BI can be used to integrate data from plant automation systems (distributed control systems, programmable logic controllers, and diagnostic monitoring systems) and provide insights to help production get the equipment operating at the ideal run rate.

An Oil and Gas company used BI to continual monitor operating characteristics of equipment such as temperature, pressure, and corrosion thickness. The visualization of real-time information enabled them to identify and repair leaks in gas lines faster. The result was a 3% increase in throughput, which translated into millions of dollars in additional revenue.

Fewer rejectsEven if a piece of equipment has no unplanned down time and is operating at its ideal run rate it still may not be delivering optimal effectiveness due to quality problems. BI can be used to track when rejects occur during a shift and job run, and identify the causes of trends and variances, such as design errors, material problems, and training deficiencies.

An Automotive Component company wanted to improve the quality of its Computerized Numeric Control (CNC) oxy flame cutting machines. Using BI to measure and analyze the causes of rejects they found both operators and maintenance personnel need additional training. After implementation of training the reject rate was reduced from 12% to 2% and non-value adding activities were reduced by 46%.

What is the financial impact?
Although the 3 KPIs of OEE are operational in nature, improving OEE also improves financial performance.

Increased revenueIf a company has $100 million dollars in sales and an overall OEE of 70%, a 1% increase in OEE would create an opportunity for an additional $1.4 million in sales.

Decreased working capitalIf two companies are identical expect one has an average OEE of 60% and one has a world class OEE of 85%, the second company will have significantly less capital tied up in equipment, work in process, and labor.

Greater profitabilityReduced down time, increased throughput and fewer rejects means less labor, overhead and material costs associated with non-value adding activity like rework, which directly impacts the bottom line.

While some might feel it's too difficult to gather all the data needed to better measure and manage OEE, the truth is that the vast majority of data already exists in shop floor systems. It's just a matter of making it easier to access in a timely fashion and presenting it in a manner that best supports decision making. And that is exactly what BI is designed to do.

http://eprints.qut.edu.au/4842/1/4842.pdf 

Condition Based Maintenance Strategy for Equipment Failure Prevention

http://www.lifetime-reliability.com/condition_based_maintenance.html 

Most equipment failures have no relationship to length of time in-service. Most failures are unpredictable. But if you detect a future failure early, you can plan and do the repair cost effectively before it becomes a breakdown.

Abstract:

Condition Based Maintenance Strategy. With only about 15% to 20% of your equipment failures being age related, and the other 80% to 85% being totally time-random events, how can you improve the uptime of your plant and facility? This article explains how to detect the random failures that make-up the vast majority of maintenance expense and production downtime by using simple, low cost condition monitoring methods.

http://en.wikipedia.org/wiki/Condition_Based_Maintenance

Condition-based maintenance
From Wikipedia, the free encyclopedia

This article or section has multiple issues. Please help improve the article or
discuss these issues on the talk page.

http://www.reliableplant.com/article.aspx?articleid=6971&pagetitle=Near-zero+downtime%3A+Overview+and+trends 

Near-zero downtime: Overview and trends

1. Maintenance Technologies Overview:

Many manufacturing companies are pushing their production equipment for every ounce of capacity while, at the same time, trying to cut their overhead costs. This has put a strong emphasis on the importance of quality maintenance services used to care for their systems. Service and maintenance are becoming essential for companies to sustain their manufacturing productivity and customer satisfaction at the highest possible level. Aftermarket support of products is increasingly becoming the key factor in determining the profitability and dependability of a company. The importance of maintenance functions, and therefore of maintenance management, has grown tremendously.

Maintenance technologies aim to

• Increase the device reliability and reduce production downtime
• Increase the throughput
• Increase life expectancy of assets
• Improve safety and quality conditions

Looking back on the development history and forecasting the development tendency of maintenance technologies, the road map to excellence in maintenance can be illustrated as in Figure 1.

Figure 1. The development of maintenance technologies.

1.1 No Maintenance

There are two kinds of situations in which no maintenance will occur.

• No way to fix it: The maintenance technique is not available for some special application, or the maintenance technique is not well-developed at the early stage.
• Isn't worth it to fix it: Some machines were designed to be used only once. Comparing with maintenance cost, it might be more cost-effective just to discard it.

None of the scenarios above are within the scope of the discussion here.

1.2 Reactive Maintenance

In plain English, the aim of reactive maintenance is just to "fix it after it's broken," since most of the time a machine breaks down without warning and it is urgent for the maintenance crew to put it back to work. This is also referred to as "firefighting".

The reason that reactive maintenance happens is because some operations have developed through the years with very little attention given to the proper care of the machinery involved. Essentially, little to no maintenance is conducted, and the machinery operates until a failure occurs. At this time, appropriate personnel are contacted to assess the situation and make the repairs as expeditiously as possible. Hence, you get the expression "putting out the fires" or "firefighting."

In a situation where the damage to equipment is not a critical factor, as plenty of downtime is available, and the values of the assets are not a concern, the firefighting mode may prove to be an acceptable option. Of course, one must consider the additional cost of making repairs on an emergency basis since soliciting bids to obtain reasonable costs may not be applicable in these situations. Due to market competition and environmental/safety issues, the trend is toward appropriating an organized and efficient maintenance program as opposed to firefighting.

1.3 Preventive Maintenance

Preventive maintenance is an equipment maintenance strategy based on replacing, overhauling or remanufacturing an item at a fixed interval, regardless of its condition at the time. Scheduled Restoration tasks and Scheduled Discard tasks are both examples of preventive maintenance tasks.

Preventive maintenance (PM) can be divided into two categories:

Minor PM is basic maintenance, which is simply the act of performing the most fundamental equipment service (lubrication, cleaning, routine adjustments, etc.), that is essential to assuring the continued operation of the equipment. This activity is quite simple with just a few machines, adequate downtime and sufficient funds. A problem begins to occur when there are a lot of machines and no organized program to schedule and control the work tasks. The solution is to implement a minor preventive maintenance program to be certain that the machinery's basic needs are addressed in a timely and efficient manner. Such a program fulfills the minimum requirement for continued operation, but does nothing to anticipate potential future failures.

Major PM not only includes Minor PM but also begins to address potential failures. With this option, machinery is scheduled to be out of service so that more involved tasks can be performed. Based on run hours or some equivalent time factor, components such as bearings, shafts, sensors, gears, piping, etc., are replaced in anticipation of potential failure in the near future. The time factor is usually determined through experience and is statistical in nature. With this practice, though, it is possible to replace components that are still in good condition as well as risking the introduction of a problem through improper maintenance. As a result, cost can sometimes increase without benefit. However, both Minor and Major PM are critical to assuring equipment reliability and so a combination of the two is frequently practiced.

1.4 Predictive Maintenance

Predictive maintenance (PdM) is a right-on-time maintenance strategy. Predictive maintenance may be best described as a process which requires technologies and people skills, while combining and using all available diagnostic and performance data, maintenance histories, operator logs and design data to make timely decisions about maintenance requirements of major/critical equipment. It is the integration of various data, information and processes that leads to the success of a PdM program. It analyzes the trend of measured physical parameters against known engineering limits for the purpose of detecting, analyzing and correcting a problem before a failure occurs. A maintenance plan is made based on the prediction results derived from condition based monitoring. This can cost more up front than PM because of the additional monitoring hardware and software investing, manning, tooling, and education required to establish a predictive maintenance program. However, it offers increased equipment reliability and a sufficient advance in information to improve planning, thereby reducing unexpected downtime and operating costs.

Figure 2 shows the different elements of the PdM program that are integrated to assist in maintenance decisions.

*Source: Augustine DiGiovanni, Vice-President CSI Services, Maintenance Optimization by Integrating Technologies and Process Change

http://www.compsys.com/enews/knewspro.nsf/v/ADIT-55JN86

Figure 2: Elements of a PdM program.

The key concepts of PdM are:

• Combine all information
• Analyze information for equipment degradation
• Determine corrective action
• Prediction algorithms
• Determine when to take corrective action
• Feedback action taken for maintenance history and/or root cause failure analysis
• Be proactive.

1.5 Proactive Maintenance

Proactive maintenance, in general terms, encompasses any tasks used to predict or prevent equipment failure. To be more specific, there are two working directions.

• Change the failure reactive to failure proactive by avoiding the underlying conditions that lead to machine faults and degradation. Proactive maintenance focuses on analyzing the root cause, and not just the symptoms. It seeks to prevent or to fix the failure from the source after it identifies the root cause. One of the most popular examples of proactive maintenance concerns heart disease in the human body. For reactive maintenance, the response will only be taken after the patient was sent to hospital emergency room. For preventive maintenance, the patient might have a bypass or transplant surgery followed by continued checkups. For predictive maintenance, heart disease can be detected using EKG or ultrasonic technology and maybe the installation of a device for continuous monitoring. For proactive maintenance, the disease control would involve cholesterol and blood pressure monitoring along with diet control.
• Feed the maintenance information back to the design and operation department. Failure prevention should also be conducted in the design and operation department. The maintenance crew's job is not only to fix a machine or change parts, but they should also help by suggesting how to improve a machine's design and operation so that the failures are prevented proactively.

There is still some debate about the efficiency and failure response speed of proactive maintenance, but there is no doubt that there has been a lack of communication between maintenance and design

1.6 Self-maintenance

Self-maintenance is a new design and system methodology. A self-maintaining machine can monitor and diagnose itself, and if any kind of failure or degradation happen, it can still maintain its functions for a while. A self-maintaining machine doesn't belong in the conventional physical maintenance concept, but in the functional maintenance concept instead. Functional maintenance aims to recover the required function of a depredating machine by trading off functions, whereas traditional repair (physical maintenance) aims to recover the initial physical state by replacing faulty components, cleaning, etc. The way to fulfill the self-maintenance function is by adding intelligence to the machine, making it clever enough for functional maintenance. In other words, self-maintainability would be appended to an existing machine as an additional embedded reasoning system.

Another system approach to creating the self-maintaining ability is to add the self-service trigger function to a machine. The machine will then self-monitor, self-diagnose and self-trigger the service request with detailed and clear maintenance requirements. The maintenance task is still conducted by a maintenance crew, but the no gap integration of machine, maintenance schedule, dispatch system and inventory management system will minimize maintenance costs to the greatest extent and raise customer satisfaction to the highest level.

2. Where Are We Now?

Most of the traditional manufacturing industries are still struggling to reduce the firefighting nature of their maintenance tasks. One major U.S. automotive manufacturer has a maintenance staff of between 15,000 and 18,000, in all their plants combined. According to them "85 percent to 90 percent [of their maintenance work] is crisis work" (breakdowns). Some other companies have already successfully adopted the preventive maintenance program in their factories. One automotive part supply company said that nearly 80 percent of their maintenance tasks are scheduled maintenance and only 20 percent are firefighting. For most of the manufacturing industries, the ideal ratio of planned to unplanned work is 19:1, which is considered to be "world class" by many of them. So, if a company already reaches a 90 percent or higher level for scheduled maintenance, from the point of view of cost saving and productivity improving, is that good enough? Actually, the key point here is whether 90 percent of the scheduled maintenance is necessary, which leads to our main discussion topic: moving from preventive maintenance to predictive maintenance.

2.1 Shift From Reactive and Preventive Maintenance to Predictive Maintenance

Reactive maintenance, performed only when equipment fails, results in both high production costs and significant service downtime caused by equipment and process breakdowns. Preventive maintenance is intended to eliminate machine or process breakdowns and reduce downtimes by scheduling maintenance operations regardless of the actual state of a machine or process. Preventive maintenance intervals are determined using reliability theory and information about the machine or process life cycle.

This practice often results in an unnecessary loss of productivity either because maintenance is performed when the process or machine is still functioning at an acceptable level, or because unpredicted breakdowns occur before scheduled maintenance operations are performed. According to a Forbes Magazine study, one out of every three dollars spent on preventive maintenance is wasted. A major overhaul facility reports that "60 percent of hydraulic pumps sent in for rebuild had nothing wrong with them." These inefficiencies are the result of maintenance performed in accordance with a schedule (fixed and guess work) as opposed to the machine's true condition and need (flexible and dynamic). So, even if we have already achieved a nearly perfect preventive maintenance level, its cost still represents a sizeable portion of the total operating expenses, and leaves a lot of room for improvement and cost savings. Therefore, in contemporary markets, it becomes increasingly important to predict and prevent failures based on current and past behavior of a piece of equipment, thus ensuring its maintenance only when needed and exactly when needed.

Preventive maintenance has always been compared to the service schedule for an automobile. If you change the oil in your car every 3,000 miles whether it needs it or not, you are following a preventive maintenance policy. The predictive maintenance is when you sample the oil from time to time and check for any changes in its characteristics and make a prediction for when your vehicle should go for service. You may find out you need to change the oil more often, or you can keep driving for another thousand miles without changing it. By using this more accurate maintenance technique, not only will you be taking better care of your automobile but you will also reduce costs by avoiding unnecessary service.

For these reasons, we propose a paradigm shift from the traditional approaches of detecting and quantifying failure toward an approach centered around detecting, quantifying and predicting the performance degradation of a process, machine or service. Performance degradation is a harbinger of system failure, so it can predict unacceptable system performance (in a process, machine or service) before it occurs. The traditional fail-and-fix practice can thus be replaced by the new predict-and-prevent process.

2.2 The Benefits of Predictive Maintenance

The benefits of predictive maintenance can be categorized as following:

1. Improve productivity

• Minimizes or eliminates costly downtime and increases profitable uptime.
• Reduces unscheduled maintenance – repairs can be made at times that least affect production.
• Optimizes machinery performance – machinery always operates within specifications.
• Reduces the time required to make machinery repairs – advance notice of machinery condition permits more efficient organization of the repair process.
• Reduces overtime required to make up for lost production due to broken down or poorly performing machinery.
• Increases the speed that machinery can be operated, if desirable.
• Increases the ease of operation of machinery.

2. Reduce the overall costs

• Reduces unnecessary machinery repairs – machines are repaired only when their performance is less than optimal.
• Reduces spare parts inventories – many parts can be purchased just in time for repairs to be made during scheduled machinery shutdowns.
• Reduces depreciation of capital investment caused by poor machinery maintenance – well maintained machinery lasts longer and performs better.
• Reduces excessive electric power consumption caused by inefficient machinery performance – saves money on energy requirements.
• Reduces need for standby equipment or additional floor space to cover excessive downtime – less capital investment required for equipment or plant.

3. Better customer relationship and satisfactory level

• Reduces the number of dissatisfied customers or lost customers due to poor quality – with less than optimal machine performance, quality always suffers.
• Just on time service reduces the customers' waiting time and downtime.
• Possibility of identifying the service demand before the customers notice the problem.
• Reduces penalties that result from late deliveries caused by broken down or poorly performing machinery.
• Reduces warranty claims due to poor product quality caused by poorly performing machinery.

4. Increases machinery safety

• Reduces the injuries caused by poorly performing machinery.
• Reduces safety penalties levied against a company for unsafe machinery.
• Reduces insurance rates because well-maintained machinery increases safety.

2.3 Requirements for Predictive Maintenance

In order to implement predictive maintenance technology two investments must be considered by the management group:

• Investment in condition-based monitoring and diagnostic equipment.
• Investment in training of staff

3. Predictive Maintenance Methodologies

3.1 Condition-Based Monitoring and Performance Assessment

The basis of predictive maintenance is condition-based monitoring. Without constantly checking a machine's operating status and tracking its tendency for degradation, it is impossible to make a precise predictive maintenance plan.

There are dozens of predictive maintenance technologies constructed on the basis of the condition-based monitoring or constant test mechanism, and some have become standards in many industries. Those standard and widely used technologies include vibration analysis, oil analysis, wear-particle analysis, ultrasound, thermography and acoustic emission analysis. The following table shows the ways maintenance professionals have traditionally used these predictive technologies for different applications.

Vibration analysis is used primarily with rotating machinery to find problems such as bearing defects, out-of-balance conditions and misalignment. Prior to the use of vibration analysis, maintenance technicians had to wait until a bearing failed to realize that there was a problem. By using vibration analysis, however, periodic readings can be taken and recorded. Maintenance personnel can then compare these readings to baseline readings. When wear reaches a certain level, the bearing is scheduled for replacement before it fails. This reduces the amount of reactive maintenance and ensures the replacement occurs with minimum impact on the production or facility schedule. In large rotating machinery, online condition monitoring systems have been widely adopted. The vibration information from each bearing section is collected and the current machine performance is evaluated based on that. Furthermore, future maintenance is scheduled according to that evaluation and its prediction of machine performance. That way, the machine would only be opened when it is really necessary.

Vibration analysis is also used to diagnose some non-mechanical problems in fluid power systems and surge or fluid excitation faults in large centrifugal compressors. For example, restrictions or disturbances in a fluid handling system create turbulence and unique vibration signatures that can help identify a problem.

Ultrasound is used primarily for leak detection, particularly for steam and air leaks. These leaks can be expensive and yet many companies allow them go unnoticed.

Common applications for ultrasound include leak detection for pneumatic and other gas systems, vacuum systems, gaskets and seals, and steam traps. Ultrasound also detects valve blow-throughs and is also the most common way to detect cavitation problems in hydraulic pumps.

Ultrasound is also used for inspections of electrical switchgear and overhead transmission lines, where routine inspection is time consuming and hazardous. These areas are monitored for corona discharge, and when the instruments "hear" the discharge, technicians can quickly find the problem with little time wasted. Thus, technicians are able to find small problems before they become critical and cause equipment failure.

Oil and Wear-Particle Analysis are two different technologies which are widely used to detect lubrication-related faults. Oil analysis determines the condition of a lubricant. Wear-particle analysis determines the condition of equipment based on the concentration of wear particles in the lubricant.

For example, consider a gear case that is showing signs of abnormal wear (e.g., noise or overheating). An oil sample could be checked for wear particles. Considering the types and condition of particles found, it is possible to isolate a number of possible problems and their causes (e.g., operating the equipment beyond design speed or capacity or filter failure). Once the problem has been identified, the appropriate maintenance action can be scheduled, again with minimum impact on operations or the facility.

Some unique applications will involve the analysis of a lubricant itself or the wear particles in the lubricant. For example, wear particles can show when there is insufficient lubrication. "Insufficient lubrication" does not necessarily mean the absence of a lubricant in a system. The lubrication system on an enclosed drive, for example, could have a clogged spray nozzle, preventing proper lubrication from reaching a hard-to-inspect area. While the visible part of the drive may be getting proper lubrication, the other area that is lacking lubrication would produce wear particles that indicate that condition. The samples can also indicate conditions such as additive failure, lubricant contamination or excessive loading that exceeds the rating of the lubricant.

Thermography is used primarily to locate electrical components that are hotter than normal. Such a condition usually indicates wear or looseness. Thus, thermography allows technicians to perform maintenance on only the electrical components that need attention without requiring that all components get the same level of attention.

In utilities, for example, the correct torque is essential on electrical components to ensure that no heat is generated from a loose connection. Before thermography, it was necessary for each connection in a control panel to be checked manually for correct torque. By using thermography, only the connections that are hot receive attention. This reduces the staff necessary to perform preventive maintenance on the connections.

Other applications include the monitoring of outdoor wiring, such as overhead transmission lines, which wear due to environmental conditions. Thermography also serves to measure transformer temperatures to find problems indicated when certain areas are hotter than others. In addition, it supports maintenance in industries that have high-temperature processes. The technology helps pinpoint areas where refractory material is wearing and allows repairs prior to catastrophic failures.

Another less-used application for thermography is checking coupling alignment without major shutdowns of the equipment. As a misaligned coupling rotates, it generates heat. The greater the temperature difference, the greater the misalignment. By using thermography, maintenance personnel can observe the temperature rise across a coupling. Some companies have used this technique long enough to develop profiles on the temperature rise for each type of coupling. Using this profile, they can determine the amount of misalignment (not what plane it is in). Then, the technicians can proactively schedule the coupling for realignment.

Acoustic emission (AE)analysis is the class of phenomena whereby an elastic wave, in the ultrasonic range usually between 20 kilohertz and 1 megahertz, is generated by the rapid release of energy from the source within a material. The elastic wave propagates through the solid to the surface, where it can be recorded by one or more sensors. The sensor is a transducer that converts the mechanical wave into an electrical signal. In this way, information about the existence and location of possible sound sources is obtained. The basis for quantitative methods is a localization technique to extract the source coordinates of the AE events as accurately as possible.

AE analysis differs from ultrasonic testing, which actively probes the structure. AE analysis listens for emissions from active defects and is very sensitive to defect activity when a structure is loaded beyond its service load in a proof test.

AE analysis is a useful method for the investigation of local damage in materials. One of the advantages it has over other NDE techniques is the potential it has to be able to observe damaged processes during the entire load history without any disturbance to the specimen.

AE analysis is used successfully in a wide range of applications including: detecting and locating faults in pressure vessels or leakage in storage tanks or pipe systems, monitoring welding applications, corrosion processes, partial discharges from components subjected to high voltage and the removal of protective coatings. Areas where research and development of AE applications is currently being pursued, among others, are process monitoring and global or local long-term monitoring of civil-engineering structures (e.g., bridges, pipelines, off-shore platforms, etc.). Another area where numerous AE applications have been published is fiber-reinforced polymer-matrix composites, in particular glass-fiber-reinforced parts or structures (e.g., fan blades). AE systems also have the capability of detecting acoustic signals created by leaks.

The disadvantage of AE analysis is that commercial AE systems can only estimate qualitatively how much damage there is to the material and approximately how long the components will last. Therefore, other NDE methods are still needed to do more thorough examinations and provide quantitative results. Moreover, service environments are generally very noisy, and the AE signals are usually very weak. Thus, signal discrimination and noise reduction are very difficult, yet extremely important for successful AE applications.

3.2 Watchdog Agent

Currently, the prevalent condition-based maintenance (CBM) approach involves estimating a machine's current condition based upon the recognition of indications of failure. Recently, several predictive CBM techniques within this failure-centered paradigm have been proposed. These approaches notwithstanding, to implement the aforementioned predictive CBM techniques require expertise and a prior knowledge about the assessed machine or process because the corresponding failure modes must be known in order to assess the current machine's or process' performance. For this reason, the aforementioned CBM methods are application specific and non-robust.

The Center for Intelligent Maintenance Systems proposed a new CBM paradigm for performance assessment and prediction based on Watchdog Agent. This new approach is based on utilizing the performance-related information obtained from the signatures extracted from multiple sensor inputs through generic signal processing, feature extraction and sensor fusion techniques. Performance assessment in this case is made based on matching the signatures representing the most recent performance with those observed during the normal system behavior. A close match between these signatures would indicate good performance, while a greater disparity between them would indicate performance degradation and the need for maintenance.

Since no failure data is needed for this CBM technique to be operational, and since the nature of the employed methods is generic, the need for expert knowledge is greatly reduced. However, if failure data describing some failure mode is available, the most recent process signatures can also be matched against those failure-related signatures with the resulting match bearing significant diagnostic information.

Figure 3 illustrates this CBM technique centered on describing and quantifying the process degradation instead of process failure. Finally, historical behavior of process signatures can be utilized to predict their behavior and thus forecast the process performance. Based on the forecasted performance, proactive maintenance is possible through the prediction of process degradation and prevention of potential failure before it occurs. Thus, the Watchdog Agent is enabled to yield the information about when unacceptable system performance will occur, why the performance degradation occurred and what component in the system needs to be maintained. This information will ultimately lead to optimal maintenance policies and actions that will proactively prevent downtime.

This entire infrastructure of multi-sensor performance assessment and prediction could be even further enhanced if Watchdog Agents mounted on identical products operating under similar conditions could exchange information and thus assist each other in building a world model. Furthermore, this communication can be used to benchmark the performance of "brother-products" and thus rapidly and efficiently identify underperforming units before they cause any serious damage and losses. This paradigm of communication and benchmarking between identical products operating in similar conditions is referred to as the "peer-to-peer" (P2P) paradigm. Figure 8 illustrates the aforementioned Watchdog Agent functionalities supported by the P2P communication and benchmarking paradigm.

Figure 3: Performance assessment based on the overlap between signatures.

According to the standard for Open System Architecture for Condition-Based Maintenance (OSA-CBM), a typical CBM system consists of the following seven layers:

• Sensor module

• Signal processing

• Condition monitoring

• Health assessment

• Prognostics

• Decision-making support

• Presentation

The Watchdog functionality expands this standard topology to a multi-sensor level and realizes sensory processing, condition monitoring, health assessment and prognostics layers of the CBM scheme. The sensors and decision making layers within an Intelligent Maintenance System are realized outside the Watchdog Agent.

Conclusion

In today's competitive market, production costs, lead time and optimal machine utilization are crucial issues for companies. Near-zero-downtime is the goal for a maintenance crew to maintain a company's throughput and high productivity. Reactive maintenance, performed only when equipment fails, results in both high production costs and significant service downtime caused by equipment and process breakdowns. Preventive maintenance is intended to eliminate machine or process breakdowns and downtimes through maintenance operations scheduled regardless of the actual state of the machine or process. Therefore, in contemporary markets, it becomes increasingly important to predict and prevent failures based on the current and past behavior of the equipment, thus ensuring its maintenance only when needed and exactly when needed.

For these reasons, the shift from the traditional reactive maintenance and preventive maintenance to predictive maintenance should be the development direction of maintenance technology. Based on the condition-based monitoring technology, the traditional fail-and-fix practice can and eventually must be replaced by the new predict-and-prevent paradigm.

About the authors:

Hai Qiu and Jay Lee help direct the NSF Industry/University Cooperative Research Center on

Intelligent Maintenance Systems (IMS) at the University of Cincinnati. To learn more, visit www.imscenter.net.

References:

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http://www.aerospaceonline.com/article.mvc/GE-And-Boeing-Implement-Industry-Stan\
dard-0001?atc~c=771+s=773+r=001+l=a&VNETCOOKIE=NO

GE And Boeing Implement Industry Standard: Open System Architecture For
Condition-Based Maintenance
June 24, 2009

GRAND RAPIDS, MICH.--(BUSINESS WIRE)--
Boeing and GE Aviation have jointly developed a simpler method to implement
condition-based maintenance systems on aircraft. It is called the Open System
Architecture for Condition-Based Maintenance (OSA-CBM). This will become an
industry standard with the signing of an agreement by the two companies to grant
rights for its use to the Machinery Information Management Open Systems Alliance
(MIMOSA) organization.

"The Boeing and GE implementation provides a 10-fold increase in real time
performance of the Open System Architecture for Condition Based Maintenance
(OSA-CBM) standard, making it practical for embedded health monitoring of
aircraft systems," said John Armendarez, president of Avionics for GE Aviation.
"This technology demonstrates a major step forward in condition-based
maintenance for an entire aircraft."

Project managers implementing condition-based maintenance systems must integrate
a wide variety of software and hardware components, each one developed to
monitor a single supplier's system such as an engine, hydraulic or braking
system. OSA-CBM simplifies this process by specifying a standard architecture
and framework to implement condition-based maintenance systems. This standard
defines the binary form to implement the open systems architecture for
condition-based maintenance.

"GE and Boeing have jointly designed and implemented these key system-enabling
technologies under shared funding," said Peter Lawrence, Boeing Research &
Technology director of Support Services. "This architecture allows aircraft and
major-aircraft-system manufacturers to economically design and deliver health
management capability within their fleets. The OSA-CBM framework provides a
standard for systems to share health information, and the new binary
implementation delivers this efficiently."

Laboratory testing in December 2008 validated the specification's operation in
both embedded and PC-based environments, across multiple computer operating
systems. The OSA-CBM framework is an important building block to what the teams
have been calling "The Health-Ready Airplane."

The aim of condition-based maintenance (CBM) is to maintain the correct
equipment at the right time. CBM is based on using real-time data to prioritize
and optimize maintenance resources. Observing the state of the system is known
as condition monitoring. Such a system will determine the equipment's health,
and act only when maintenance is actually necessary.

Development in recent years has allowed extensive instrumentation of equipment,
and together with better tools for analyzing condition data, the maintenance
personnel of today are more than ever able to decide when the right time to
perform maintenance on some piece of equipment is. Ideally, CBM will allow the
maintenance personnel to do only the right things, minimizing spare parts cost,
system downtime and time spent on maintenance.

http://www.systecon.se/documents/RailSparesStrategy.pdf

What assortment of components and spares will be needed for a train operation
when the low-frequent maintenance starts?

How can the choice of maintenance strategy possibly impact on the size of this
investment?

These are questions that all fleet operators and owners face from time to time.

http://www2.emersonprocess.com/en-US/news/pr/Pages/907-CSI9210.aspx

The CSI 9210 Machinery Health Transmitter now delivers predictive machinery
health diagnostics for all types of rotating machinery

KNOXVILLE, TENN (July 1, 2009) --  Emerson announces extension of the monitoring
capabilities of its CSI 9210 Machinery Health Transmitter. The CSI 9210, which
monitors vibration, temperature, and speed on machine trains, can now be applied
more extensively to plant and mill rotating machinery, including motors, fans,
cooling tower fans, pumps, and compressors. A component of PlantWeb® digital
plant architecture, the smart transmitter analyzes the health of mechanical
equipment through predictive diagnostics to improve plant availability and
performance.

Condition monitoring diagnostics from the CSI 9210 give plant personnel a better
understanding of equipment health and developing issues. This knowledge empowers
predictive maintenance practices, resulting in less equipment downtime, longer
machine life, and lower maintenance costs.

http://www.reliableplant.com/article.aspx?articleid=19036

Video: U.S. Navy shifts toward condition monitoring

Another Tool for Predictive Maintenance - Enhanced Location Recording Embedded
in Post-Processing Software

http://www.railway-technology.com/contractors/track/cater/press4.html

http://cater.fcpl.com.au/

Som vanligt är Australien ett steg framför andra länder kring CBM.

:-)

Slutar snackar, utan gör….


Göran W.