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Under the Covers with EXAKT

How it works…..

July 2009 --- Ben Stevens

Ben @omdec.com www .omdec.com

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Agenda

• Thinking about data• Data Pre-processing

– Data Validation– Data Smoothing– Data Analysis

• EXAKT Analysis– Modeling for single dominant Failure Mode– Modeling for complex items (2+ Failure Modes or multiple

components)– Cost Modeling– Sensitivity Analysis– Output Reports

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Thinking about data: General Problem

• Each equipment, and each failure mode for each equipment is potentially unique.

• What we need to measure are those variables which are important as predictors of failure.

• Thus we cannot provide a definitive list of the data set that is going to be required for an equipment problem (failure mode) until that equipment and failure modes have been determined.

• The analogy is trying to predict what procedures will be needed to do service work on a vehicle without knowing what type of vehicle will be serviced (car, truck, bus, train….), and what the problem is.

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Thinking about data: General Answer

• Three key sources of data:– CBM data – oil sample analysis (what particles are in

the oil and how much), vibration data, temperature data – whatever data can be used to help predict failure

– Operating data – operating hours, load, power cycles– Event data – what has been done to the equipment

which might affect the reliability of the data – parts swap-outs, suspensions from service, service jobs, oil changes

+ if available, RCM analysis (this will help to define the failure modes)

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Thinking about data: Amount of data

• Too much data is rarely a problem; missing data almost always is.

• Minimum of about 4 failures of any given failure mode – (8+ is better)

• Too many co-variates is not a problem – EXAKT can sort out which are zero-impact or low impact in terms of ability to predict failure

• Too little data, (or inconsistent data) will show up as low confidence levels – which makes the prediction not much use (50% CL = flip a coin)

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Thinking about data: Simple Examples“Failure Modes” CBM data Operating data Event Data

Tire failure Tire Pressure, speed, operating temperature, wheel balance/vibration.

Load, distance, tire compound, road surface

Install dates, repair dates, rotation dates, suspensions

Bearing failure Vibration, oil sediment count

Operating hours, Load Install dates, Lubrication dates, equipment suspensions

Pump failure Vibration, oil sediment count, operating temperature

Operating hours, product pumped

Install dates, service dates, equipment suspensions

Pipeline failure Pressure, wall-thickness measure-ment, temperature

Operating hours, product pumped, construction material

Install date, clean out dates

Note – these “Failure Modes” are actually groups of failure modes for the purposes of examples; the specific co-variate data required to predict failure will depend on the specific failure mode

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Data Validation1. Objective – sufficient, consistent, accurate data2. Initial Data Analysis

1. # Items, # Histories2. Total Event Beginnings versus Total Event Ends

3. Typical Problems identified by EXAKT1. Missing End Event (manually fill)2. Duplicate records (eliminate one)3. Two events on same Working Age but different dates (verify

machine idle period)4. Multiple repeat readings (measurement error equipment or

human reading)5. Missing record (interpolate)6. Unexplained peaks and valleys (verify or discard)

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Data Smoothing

• Objective – eliminate outliers, make sense of variances from a trend

• Erratic data recordings (but with overall increasing or declining trend)– Smoothing data readings by linear regression– Smoothing data averages – Performed by EXAKT

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Data Analysis

• Objectives:– to read the patterns in the data before transferring to EXAKT; – establish the impact of underlying trends before modeling;– Establish dependence/independence– Decide on derivatives

• Examples:– Correlation analysis/investigation of relationships among variables– Correlation between inspections and events– Data reduction/consolidation/ summarization– Variable transformations (derived variables) – Lagging and leading variables– History transformations– Signal processing.

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Modeling for single dominant Failure Mode

Objectives:1. Target high cost equipment, equipment with

high cost of failure

2. Minimise cost (Preventive Maintenance + Failure Cost) – or

3. Maximise Availability – or

4. A combination

5. Establish Remaining Useful Life

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Modeling for single dominant Failure Mode –2

General Procedure:1. Create Inspection Table

1. shows condition data from CBM measurements at each working age

2. Working age = cycles, tons produced, operating hours x stress3. (download from CBM db, type from inspection reports, extract

from CMMS…)

2. Create Events table1. Any event which has an impact on reliability2. B = Beginning, EF = End in Failure, ES = End in Suspension (ie

take off line), OC = Oil change etc3. (extract from CMMS)

3. Combine Events table with Inspections

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Modeling for single dominant Failure Mode – 3

General Procedure:4. Proportional Hazards Modeling

1. Objective is to determine which conditions have predictive capacity, and eliminate non-predictive conditions AND to determine the relative significance of their contribution to the prediction

2. Select variables (in EXAKT)3. Wald test automatically ranks the impacts of each variable and shows

Y or N impact and the probability of their being NO IMPACT4. From the N’s, eliminate highest P-value (lowest impact)5. Re-run and repeat until all N’s deleted6. Note with P-scores similar for different variables, may conclude

several different models are valid.7. If so, run Comparative Report to distinguish among them

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Modeling for single dominant Failure Mode – 4

General Procedure:5. Goodness of Fit

1. Kolmogorov-Smirnoff tests the accuracy of the prediction – 95% significance (ie 19 times out of 20)

2. If not significant….. Either search for missing variable and/or more data on existing variables or more consistent data

6. Transition Probability model1. EXAKT sets bands for condition readings (ex: 5 to 10ppm)

2. TP model shows the probability of jumping from 1 defined band to the next highest before the next inspection period; (helps to establish the probability of failure)

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Modeling for single dominant Failure Mode – 5

General Procedure:7. Cost Model

1. Sets CR (Cost Ratio) – ratio of Failure Cost to Preventive Replacement Cost

8. Outputs for group of assets9. Re-run model for single asset with latest data10. Review reports and recommend action

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Modeling for complex items

• Complex item= 2+ Failure Modes or multiple components

– Requires Marginal Analysis– Performed within EXAKT– Distinguishes among FM’s– Shows which FM is most imminent

1. Prepare separate models for each component or Failure Mode

2. Examine and compare output reports3. Perform maintenance action

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Cost of Preventive Replacement= Cost of PM Work + Cost of Lost Mission Readiness, lost Revenue or Profit during the PM + Penalty Costs, Reputation Costs, Fines and Reparations

during the PMCost of Failure = Cost of Emergency Repair + Cost of Lost Mission Readiness, lost Revenue or Profit+ Penalty Costs, Reputation Costs, Fines and Reparations

Cost Modeling1. EXAKT calculates CR (Cost Ratio) – Ratio of Failure

Replacement to Cost of Preventive Replacement2. Numbers typically used are ~ 3:1 to 10:1. One example

of 1000:1

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Sensitivity Analysis

• Objectives:– How sensitive are our conclusions to variance in cost

and time? – How accurate do we have to be to get reliable results?

• Built into EXAKT• Examples:

1. Sensitivity of Preventive:Replacement cost ratios2. Sensitivity of Preventive:Replacement time ratio3. Downtime costs

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Output Reports

1. Traffic light graph

2. Failure Risk plot

3. Conditional Failure Distribution

4. Conditional Density of Failure (frequency against working age)

5. Cost Report

6. Availability Report

7. Cost and Availability Report

8. Cost and Hazard Sensitivity reports

9. Time to Replace Report

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Traffic light graph

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Failure Risk plot

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Conditional Failure Distribution Function

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Conditional Density of Failure (frequency against working age)

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Cost Report

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CR=3

CR=5

CR=6

Comparative Cost Reports

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Availability Report

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Cost and Availability Report

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Hazard Sensitivity Example

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Cost Sensitivity Example

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Remaining Useful Life

RUL

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Questions?