7 a New Look at Criticality Analisys for Machinery Lubrication

2 | March - April 2013 | www.machinerylubrication.com

M a i n t e n a n c e a n d R e l i a b i l i t y

For decades, reliability scholars have been stressing the importance

of prioritizing new maintenance thrusts and investments based on need. The word they like to use is “criticality.” For any given machine, how critical is its reliability? What if it failed suddenly and catastrophically? What would be the consequences — lost production, expensive repairs, fatality? Criticality is the logical starting point for all reliability initiatives.

There are many different ways to enhance reliability and improve the quality of mainte-

nance. The best options should be risk-based. After all, if it doesn’t reduce risk, why do it? Why spend an incremental dollar to enhance a machine’s reliability if it doesn’t yield multiple dollars in return?

There’s also priority. What should be done ! rst, second and third, and what should not be done at all? How do you know which machines return big dollars for enhanced reliability, which machines return marginal dollars and which machines return nothing at all?

Once you understand machine criticality

and a machine’s risk pro! le, you can work smarter to customize improvements. For guidance, look to the Pareto principle, which states that 20 percent of the machines cause 80 percent of the reliability problems. Which machines are these?

In addition, consider that 20 percent of the causes of failure are responsible for 80 percent of the occurrences of failure. Which causes are these? It’s about precision — precision maintenance and precision lubrication. It’s also knowing how to make wise, risk-informed choices.

AS I SEE IT

A New LOOK at CRITICALITY Analysis for Machinery LUBRICATION

JIM FITCH NORIA CORPORATION

Figure 1. Machine Criticality Factor (MCF) (Relates to the consequences of machine failure)

I’ve written previously about the Optimum Reference State (ORS). This is the prescribed state of machine con! guration, operating condi-tions and maintenance activities required to achieve and sustain speci! c reliability objectives. As stated, de! ning the ORS requires a de! nition of the speci! c reliability objectives for a given machine. De! ning the reliability objectives demands an understanding of failure modes and machine criticality.

This reminds me of the plant manager who told me years ago that he decided the best way to solve his lubrication problems was to put synthetic lubricants in every machine. Do you think he got the result he sought? Does paying a premium for synthetics guarantee a premium return in machine reliability and maintenance cost reduction? Do synthetics offer forgiveness for negligent and shoddy maintenance? Is this wise decision-making?

Understand the Reliability-Risk Connection

The probability of machine failure needs to be inversely proportional to risk. There’s no better example than commercial aviation. Because the consequences of failure are extremely high (death), the probability of failure

must be equally low (extreme reliability). It is the only practical means to hedge risk. Those respon-sible for maintenance usually have little control over the consequences of failure (often limited only to early detection technology). However, reli-ability maintainers frequently have considerable control over the probability of failure. Indeed, you can use risk and criticality to develop a master plan for lubrication-enabled machine reliability. This will be the focus of this article.

Let’s begin with a list of common lubrica-tion and oil analysis decisions (all attributes of the ORS) that can be customized (opti-mized) by understanding failure modes and machine criticality:

• Lubricant selection, e.g., premium vs. econ-omy-formulated lubricants

• Filtration, including things such as ! lter quality, pore size, capture ef! ciency, location and " ow rate

• Lubricant preventive maintenance (daily PMs) and inspection strategy

• Lubricant delivery method selection and use (e.g., circulating, auto-lube, mist, etc.)

• Oil analysis (which machines are included and which are not?)

Machinery

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MACHINERY LUBRICATION Volume 13 - Issue 2 March-April 2013 ( USPS 021-695) is published bimonthly by Noria Corporation, 1328 E. 43rd Court, Tulsa, OK 74105-4124. Periodicals postage paid at Tulsa, OK and additional mailing of! ces. POSTMASTER: Send address changes and form 3579 to MACHINERY LUBRICATION, P.O. BOX 47702, Plymouth, MN 55447-0401. Canada Post International Publica-tions Mail Product (Canadian Distribution) Publications Mail Agreement #40612608. Send returns (Canada) to BleuChip Interna-tional, P.O. Box 25542, London, Ontario, N6C 6B2.

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Copyright © 2013 Noria Corporation. Noria, Machinery Lubrication and associated logos are trademarks of Noria Corporation. All rights reserved. Reproduction in whole or in part in any form or medium without express written permission of Noria Corporation is prohibited. Machinery Lubrication is an independently produced publication of Noria Corporation. Noria Corporation reserves the right, with respect to submissions, to revise, republish and authorize its readers to use the tips and articles submitted for personal and commercial use. The opinions of those interviewed and those who write articles for this magazine are not necessarily shared by Noria Corporation.

CONTENT NOTICE: The recommendations and information provided in Machinery Lubrication and its related information properties do not purport to address all of the safety concerns that may exist. It is the respon-sibility of the user to follow appropriate safety and health practices. Further, Noria does not make any representations, warranties, express or implied, regarding the accuracy, completeness or suitability, of the information or recommendations provided herewith. Noria shall not be liable for any inju-ries, loss of pro! ts, business, goodwill, data, interruption of business, nor for incidental or consequential merchantability or ! tness of purpose, or damages related to the use of information or recommendations provided.

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March - April 2013 | 3

FAILURE OCCURRENCE FACTOR (FOF)

FAILURE OCCURRENCE FACTOR

METHOD A.MACHINE RELIABILITY HISTORY IS KNOWN

METHOD B.MACHINE RELIABILITY IS UNKNOWN

1 NeverMachine has long history, has never been known to fail and is showing no signs of impaired reliability.

2 Very RareMachine is highly reliable, and past failures have been extremely rare (15+ years of service life).

3 Rare Machine can go more than 10 years without failure.

4 InfrequentMachine has been known to fail but only after 5 or more years.

5 Occasional Failures can occur in the time range of 3 to 8 years.

6Common and Likely

Failures are likely after 3 to 5 years’ service life.

7Somewhat Frequent

Failures tend to occur after 2 to 5 years’ service life.

8 Frequent Failures tend to occur after 1 to 3 years’ service life.

9Very Frequent

Failures occur frequently in 0.5 to 2 years’ service life.

10Chronic and Certain

Failures are expected in less than 1 year’s service life.

Complete The Reliability

Elements Quotient

Figure 2. Use this table to determine the Failure Occurrence Factor, corresponding to the probability of failure.


AS I SEE IT

• Oil sampling frequency (weekly, monthly, quarterly, never)

• Laboratory and test slate selection

• Oil analysis alarms and limits

All of these decisions and activities must be within the scope of the Optimum Reference State. For this reason, the importance of criticality should not be taken lightly. However, a practical means of assigning a value to criticality, customized to machine lubrica-tion and tribology, has largely been elusive. In fact, the ! elds of lubrication and tribology raise unique issues and questions related to criticality that aren’t typically addressed and aren’t common to other types of machinery.

Calculating Overall Machine CriticalityOverall Machine Criticality (OMC) is a risk-pro! le assessment

that can be calculated to a single numerical value. The OMC is what you seek to know and control. The lower the OMC, the lower the risk. The OMC is the multiplied product of two factors: the Machine Criticality Factor (MCF) and the Failure Occurrence Factor (FOF). The MCF relates to the consequences of machine failure, which combines both mission criticality and repair costs, while the FOF relates to the probability of machine failure. This probability is highly in" uenced by maintenance and lubrication practices and therefore is far more controllable.

Machine Criticality FactorA simple method for estimating the Machine Criticality Factor is

shown in Figure 1. It requires an understanding of mission criticality

and repair costs. While you could call these SWAGs (educated guesses), it is far better to guess using a logical method than to apply dartboard science or do nothing at all.

The MCF is scaled 1 to 10, with 10 corresponding to extreme criticality (high risk). You start by answering the question of mission criticality. Machines that are process-critical can accumulate huge production losses as a result of sudden and prolonged failure. Extremely high mission criticality relates to safety (injury or death). In the event there is minimal business interruption or safety risk, there might still be high repair costs. Although many processes have redundant systems or standby equipment in the event of failure, these systems don’t mitigate the cost of repair, which can be millions of dollars in some circumstances.

The ! nal consideration is the current or potential use of early detection technology (predictive maintenance) to annunciate alarms of impending or precipitous failure events. In such cases, both downtime and the cost of repair can be substantially reduced. Oil analysis (wear debris analysis), vibration analysis, bearing metal temperatures, proximity probes, motor current, etc., are all tech-nologies that can offer real bene! t in reducing the Machine Criticality Factor (see the adjusted scale at the bottom of Figure 1, which applies only if effective early warning systems are used).

Failure Occurrence FactorAs mentioned previously, the Failure Occurrence Factor relates

to the probability of machine failure. This can be estimated from the machine’s failure history or statistical analysis of a group of identical machines. Machines that are inherently prone to failure

Figure 3. An example of a pre-ORS Reliability Elements Quotient.

www.machinerylubrication.com | March - April 2013 | 5


MACHINE CRITICALITY FACTOR

1 2 3 4 5 6 7 8 9 10FA

ILU

RE O

CC

UR

RE

NC

E F

AC

TO

R

1 1 2 3 4 5 6 7 8 9 10

2 2 4 6 8 10 12 14 16 18 20

3 3 6 9 12 15 18 21 24 27 30

4 4 8 12 16 20 24 28 32 36 40

5 5 10 15 20 25 30 35 40 45 50

6 6 12 18 24 30 36 42 48 54 60

7 7 14 21 28 35 42 49 56 63 70

8 8 16 24 32 40 48 56 64 72 80

9 9 18 27 36 45 54 63 72 81 90

10 10 20 30 40 50 60 70 80 90 100

AS I SEE IT

Color RiskRemediation Required

RedExtreme Risk

Immediate

Amber High Risk High Priority

YellowManageable Risk

As Soon as Possible

Green Minor RiskContinuous Improvement

Blue Low Risk None

Figure 4. The Overall Machine Criticality (OMC) matrix includes the Machine Criti-cality Factor on the X-axis, the Failure Occurrence Factor on the Y-axis and five risk zones, each represented by a different color.

(bad actors) get the highest rating on a scale of 1 to 10. High FOFs usually correspond to extreme and chronic conditions (see the table in Figure 2). If you have good historical knowledge of the machine’s reliability, then use the descriptive rating scheme (Method A) under the “Machine Reliability History is Known” heading. If machine reliability is unknown or uncertain, go to the Reliability Elements Quotient (REQ) in Figure 3 (Method B). This is a scoring system that shows what causes and controls failure in lubricated machines. Most importantly, it reveals the funda-mental strategy for optimizing machine reliability.

Reliability Elements QuotientThe REQ (Figure 3) tallies ! ve critical elements to arrive at a

customized composite score that will be used for the FOF in Figure 2. It gets down into the weeds of what causes a greater or lesser likelihood of machine failure. Let’s discuss these elements, starting at the top and working our way down.

• Machine Duty - Machine duty is a compilation of opera-tional conditions that can induce premature machine failure. Machines that score high are those that run at or beyond rated loads (catalog loads), operate at high pressure, run at high speed, are exposed to high shock loads or duty cycles, and have other similar mechanical conditions.

• Lubricant Quality/Performance - Good lubricant selec-tion extends machine life, while poor lubricant selection shortens it. The bene! t of good lubricants not only reduces friction and wear but can also protect the machine from corrosion, air entrainment, deposit formation and lubricant starvation. Therefore, lubricant quality directly in" uences the probability of failure.

• Lubrication Effectiveness - More machines fail due to poor lubrication than poor lubricants. Lubrication relates to a range of activities and conditions including relubrication frequency, relubrication method, controlling lubricant levels, lubrication procedures, inspection methods and contamina-tion control. For most plants, there is a large gap between doing lubrication and doing lubrication right.

• Fluid Environment Severity - This is largely contamination control related. Contamination compromises the quality of the lubricant and the state of lubrication. It relates to what the machine is exposed to in its work environment (and the severity of exposure), plus the effectiveness of the machine in excluding and removing contaminants from the lubricant. Machines that are bombarded with dirt, water, corrosive materials, ambient heat/cold and process chemicals have high " uid environment severity.

• Early Warning Systems - Early warning technology also impacts the probability of failure. This is done by catching incipient failures or root-cause conditions that are the precur-sors to failures. Oil analysis and comprehensive daily machine inspections are extremely effective at providing early warning to a host of problems.

The Reliability Elements Quotient is a scorecard that counts all ! ve factors. For each element, the score range goes left to right, from very low (far left) to extremely high (far right). The numerical scale changes for each factor. The best way to use the REQ is to circle the assigned score for each factor and then write the score in the box to the right. The total score is tallied at the bottom. In the example, this total is 8, which designates high failure probability.

Overall Machine Criticality Matrix

and De-Risking Your PlantThe OMC is probably best viewed as a matrix. This is shown in

Figure 4 with the MCF on the X-axis and the FOF on the Y-axis. The intersecting box reveals the OMC value (multiplication of the MCF and the FOF). The matrix has ! ve color zones which are actually risk zones (the location of these zones on the grid can be custom-ized). The highest risk is represented by the color red. Next is amber, followed by yellow, then green and ! nally blue (low risk).

Machines that fall in the amber or red zones are targeted for immediate remediation. This is best done by reducing risk values from one or more of the four “addressable” reliability elements (see Figure 3), which are subcomponents of the FOF. These are lubricant


quality/performance, lubrication effectiveness, " uid environment

severity and effectiveness of early warning systems.

This is exactly the purpose of the Optimum Reference State.

Figure 5 shows how key ORS performance attributes in" uence the

addressable reliability elements that in turn in" uence Overall

Machine Criticality. Everything is connected.

Additionally, failure modes and effects analysis (FMEA) can be

used to assign priority to ORS attribute improvements. For more

information on FMEA as it applies to machinery lubrication, see

http://www.machinerylubrication.com/Read/17/fmea-process.

It makes sense that all reliability initiatives need to adjust

(improve) the OMC. This typically involves a range of modi! cations

to the ORS performance attributes as shown in Figure 5. These can

include machinery modi! cations, lubricant selection changes,

people skills improvements, procedure modi! cations and others.

“Optimizing” the modi! cation master plan through FMEA and

criticality analysis achieves the lowest risk pro! le or OMC at the lowest possible cost.

An example of this is seen in Figures 6 and 7. By making modi! -cations to lubricant selection, lubrication methods, contamination control and oil analysis, the Failure Occurrence Factor improved from 8 to 1. For a machine that has a Machine Criticality Factor of 5, this brought the risk pro! le down from 40 (amber, high-risk zone) to 5 (blue, low-risk zone).

What It All MeansIn the January-February 2013 issue of Machinery Lubrication, I wrote

about the Technology Adoption Cycle and the impediments to adop-tion of the Optimum Reference State. People, especially managers, “go with what they know.” If they don’t understand risk and reward as it relates to machine reliability, they will shy away from acceptance and adoption. The state of lubrication continues “business as usual.” This is a curse indeed, but one that can be remedied.

* Process design and control influence, not usually maintenance related

Figure 5. This table shows how the ORS performance attributes directly influence the elements in the Reliability Elements Quotient (REQ).

ORS PERFORMANCE ATTRIBUTES

ADDRESSABLE RELIABILITY ELEMENTS

MACHINE DUTY*

LUBRICANT QUALITY/ PERFORMANCE

LUBRICATION EFFECTIVENESS

FLUID ENVIRONMENT SEVERITY

EFFECTIVENESS OF EARLY WARNING SYSTEMS

Lubricant Attributes

Optimum lubricant products and supplier selection

Lubricant reception, labeling, packaging, storing and handling

Lubrication Attributes

Optimum selection of oil change and regrease intervals

Optimum selection, documentation and use of lubri-cation and oil analysis PMs, tasks and procedures

Machine Attributes

Proper selection and location of filters

Correct selection and location of oil level gauges and inspection sight glasses

Correct selection and location of sampling valves

Optimum selection of breathers and headspace management devices

Correct machine relubrication and flushing hardware and tools

Optimum selection and use of seals and leakage control devices

Optimum selection and use of seals to control contaminant ingression

Oil Analysis Attributes Oil analysis program design and execution

People and Program Management

Attributes

Awareness training, skills training, competency testing

Optimum use of lubrication program metrics and KPIs

Optimum program management, data management, work management systems

= Major Influence

= Moderate Influence

= Minor Influence


An excellent place to start is by developing a current risk pro! le of your critical machinery (pre-ORS). This reveals the opportunity and all the low-hanging fruit that no one has seemed to notice. Optimum is unde! nable without understanding risk. By using the tools described here, you not only can understand risk (criticality and occurrence), but you can also have a solid plan for remediation to de-risk your plant. Don’t fail to capitalize on the riches (collect the fruit) that can be gained by transformation to the Optimum Reference State.

About the Author Jim Fitch has a wealth of “in the trenches” experience in lubrication,

oil analysis, tribology and machinery failure investigations. Over the past

two decades, he has presented hundreds of courses on these subjects. Jim

has published more than 200 technical articles, papers and publications.

He serves as a U.S. delegate to the ISO tribology and oil analysis working

group. Since 2002, he has been director and board member of the Interna-

tional Council for Machinery Lubrication. He is the CEO and a co-founder

of Noria Corporation. Contact Jim at j! [email protected].

AS I SEE IT

PRE-ORS POST-ORS

Machine Criticality Factor (MCF) 5 5

Failure Occurrence Factor (FOF) 8 1

Machine Duty 3 3

Lubricant Quality/Performance 2 1

Lubrication Effectiveness 1 0

Fluid Environment Severity 3 0

Effectiveness of Early Warning Systems

-1 -3

Overall Machine Criticality (OMC)* 40 5

OMC Zone Amber Blue

OMC Risk High Risk Low Risk

* OMC=MCF x FOF

Figure 8. Illustration of how bringing a machine to the Optimum Reference State can reduce risk.

Figure 7. This post-ORS Reliability Elements Quotient shows how the Failure Occurrence Factor improved from 8 to 1 after several modifications were made.

MACHINE CRITICALITY FACTOR

1 2 3 4 5 6 7 8 9 10

FA

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1 1 2 3 4 5 6 7 8 9 10

2 2 4 6 8 10 12 14 16 18 20

3 3 6 9 12 15 18 21 24 27 30

4 4 8 12 16 20 24 28 32 36 40

5 5 10 15 20 25 30 35 40 45 50

6 6 12 18 24 30 36 42 48 54 60

7 7 14 21 28 35 42 49 56 63 70

8 8 16 24 32 40 48 56 64 72 80

9 9 18 27 36 45 54 63 72 81 90

10 10 20 30 40 50 60 70 80 90 100

Figure 6. This OMC matrix illustrates how improvements in lubricant selection, lubrication methods, contamination control and oil analysis brought a machine’s risk profile down from 40 to 5.

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7 a New Look at Criticality Analisys for Machinery Lubrication

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