Making Evidence-Based Maintenance Decisions

www.ipamc.org

Making Evidence-Based Maintenance Decisions

Andrew K S JardineCBM Laboratory

Department of Mechanical & Industrial EngineeringUniversity of Toronto

[email protected]

August 2006

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www.ipamc.orgAndrew Jardine, CBM Lab

“Making Evidence-Based Maintenance Decisions”

ComponentReplacement

Maintenance Management System (CMMS/EAM/ERP)

Capital EquipmentReplacement

InspectionProcedures

ResourceRequirements

Excellence in Physical Asset Management

Optimizing Equipment Maintenance and Replacement Decisions

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Asset Management

We want

Fact–based arguments(data driven decisions)

not

Intuition–based pronouncements(strength of personalities, # of mechanics’ complaints)

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RCM Methodology LogicSELECT

EQUIPMENT

Is condition monitoring technically and economically feasible to detect warning of a

gradual loss of the FUNCTION?

Is a repair technically and economically feasible to restore the performance of the item, and will

this reduce the risk of FAILURE ?

Is it technically and economically feasible to replace the item, and will this reduce the risk of

FAILURE ?

Condition-BasedMaintenance

Time-BasedMaintenance

Default Actions

Time-BasedDiscard

YES

YES

YES

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“Once the equipment enters service a whole new set of information will come to light, and from this point on the maintenance program will evolve on the basis of data from actual operating experience. This process will continue throughout the service life of the equipment, so that at every stage maintenance decisions are based, not on an estimate of what reliability is likely to be, but on the specific reliability characteristics that can be determined at the time.”

F.S. Nowlan and H. Heap

Back to Basics

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EQUIPMENT






FAILURE ?



Time-BasedDiscard

YES

YES

YESDefault Actions

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Toronto Transit Commission:

Subway system“At the TTC, I have been able to analyze several components (which we were overhauling periodically) to justify if it is worthwhile doing the overhaul. It was possible to use Weibull analysis since when these components failed, they failed due to a dominant failure mode, and the defective component was replaced with a new one or one that is just like new. I found that most times the hazard rates obtained were decreasing. This I later found was due to poor quality components and questionable maintenance practices. Overhaul on these components has been suspended and we are only changing them on failure. Quality issues are also being addressed.”

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Using MTBF to Determine Maintenance Interval Frequency is Wrong

“Random failures make up the vast majority of failures on complex equipment as research has shown. For example, consider the failure of a component. Assume that each time the component failed we tracked the length of time it was in service. The first time the component is put into service it fails after 4 years, the second time after 6 years, and the third time after only 2 years (4 + 6 + 2 = 12/3 = 4). We know that the average lifespan of the component is 4 years (its MTBF is 4 years).

However, we do not know when the next component will fail.Therefore we cannot successfully manage this failure by traditional time-based maintenance (scheduled overhaul or replacement).”

Source: Viewpoint: Maintenance Technology, October 2003

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“Making Evidence-Based Maintenance Decisions” © CBM Lab

Fact 1. Preventive replacement at the MTBF could be the best answer, but it does depend on additional evidence.

Fact 2. If a reliability engineer trained in the statistical analysis of failures analyzed the 3 failure times they would obtain a "best-estimate" that there is significant wear-out occurring, and that time-base replacement could be appropriate.

This conclusion is obtained by examining the evidence (3 failure times) and doing a simple Weibull analysis. Using regression analysis the shape parameter, beta, is estimated as 1.74. Thus the “best estimate”indicates an increasing hazard function, and so the risk of bearing failure occurring could be reduced through bearing preventive replacement based on time.

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Make evidence based decisions!!

- Using appropriate tools

MORAL

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Capital Equipment Replacement



Maintenance Excellence


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Dear Professor Jardine,

We are one of the largest marine cargo handling firms in the U.S. We have approx 2400 pieces of rolling stock, mostly powered lift equipment (stationary cranes, mobile cranes, side & top handlers, forklifts, etc). We have no corporate strategy on equipment repair/replacement, lease vs. buy, economic service life, etc. These decisions are based often on strength of personalities and # of mechanics complaints, not objective analysis. I'm looking to change that. On the plus side, we do have a CMMS (Maximo) and 4 years of "pretty

good" equipment information and cost history. So we have some data to analyze.I'll be back in my office Sept 18-19, perhaps we could connect then. I'm on U.S. west coast time (based in Los Angeles). Look forward to learning more.

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The outcome of the previous message was that the company was visited for one day.

In the morning a procedure to establish the economic life of their mobile equipment was discussed.

In the afternoon the IT person joined the discussion, and discussed how to access their company data base.

The data from the data base was then inputted into a standard economic life model to establish the economic life for a sample asset -it was a Hustler truck - costing about USD 60,000 new.

Company present policy was to replace their Hustlers at about 18 years of age.

The economic life established by using the economic life model was about 10 years. Cost saving per year was USD 3340.

There were 449 similar vehicles in their fleet.Therefore total annual saving was estimated at:

USD 3340.00 x 449 = USD 1.5 millions PER YEAR

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Maintenance Excellence


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EQUIPMENT






FAILURE ?



Default Actions

Time-BasedDiscard

YES

YES

YES

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100%

Measurable property

Potential failure

Functional failure

TimePF Gap

Detectable deterioration

The THEORY:

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The REALITY:

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WorkingAge

Normal < 200ppm

Warning > 200ppm

Alarm > 300ppm

• Simple to understand

• Limitations:– Which

measurements?– Optimal limits?– Effect of Age?– Predictions?

• EXAKT extends and enhances the Control Chart technique

Classical Approach: Warning Limits

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Data Plot

Age

Data

Hazard Plot

Age

Hazard PHM

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OPTIMAL POLICY - OPTIMAL HAZARD LEVEL

Age

Hazard

Hazard

Cost/unit time

Cost Plot

Ignore Hazard

Replace at failure only

minimal cost

Hazard Plot

Optimal Hazard level

Optimal hazard

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EXAKT Optimal Decision –A New “Control Chart”

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Analysis of Shear PumpBearings Vibration Data

21 vibration measurements provided by accelerometer

Using PHM & Costs

3 measurements significantSavings obtained = 35 %

Campbell Soup Company: Executive Summary

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Failed at WorkingAge = 182 days

Inspection at Working Age = 175 days

Had we replaced at 175 days…..!!!

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Maintenance Optimization


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Alternative service deliverer’sprocessingCost/unit time

Internal processingcost/unit time

Fixed cost/unit time

Total cost/unit timeOptimallevel of

maintenance resource

Level of Maintenance Resource

Cos

t

Optimal Contracting-out Decision

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Fact–based arguments(data driven decisions)

not

Intuition–based pronouncements(strength of personalities, # of mechanics’ complaints)

We want

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We haveTools to deliver

Fact – based arguments

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A SuggestionLet us develop our evidence based maintenance tool box.

A collection of tools for identifying, assessing and applying relevant evidence for better asset management decision-making.

It is important to have evidence to support asset management programs and not simply accept“expert opinion.”

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THANK YOUUniversity of Toronto Research Lab:

www.mie.utoronto.ca/cbm

Documents

Making Evidence-Based Maintenance Decisions