Reliability from DATA

Reliability from DATA

A framework for technology

OMDEC

1. Maintenance / Asset Management Consulting2. Training Programs3. Software Tools4. “Living RCM”5. Canadian Company: Ottawa, Montreal, Toronto,

and Australia Locations

Sample Industries: Mining, Oil & Gas, Utilities, Fleets, Government and Military

Why collect data? Only one reason: To perform analysis. -

“Reliability Analysis” Why analyze?

To improve the process of maintenance continuously. (CPI = Continuous Process Improvement)

Why CPI? That’s our (i.e. everyone’s, particularly

management’s) job. Why?

Economic survival of the fittest. Keep up with change.

The “false” promise of CBM technology

Based on the logic that: The more data the better, The faster the better, and The more views (PDAs, iPhone, etc) the better. All of the above are good, but there is a flaw in the

logic.

What is the logical flaw? There is an infinite supply of the wrong data. The logic skirts the question: “What is the right data?”

What’s the right data? Age (“life”, “life cycle”, “event”) data

Failure Mode occurrences with attributes: event type (PF, FF, S, …), RCM reference, working age

Condition monitoring data relevant to the failure modes of interest.

RCM knowledge of failure modes.

Work orders RCM

Achieving reliability from data

1. Data extraction and transformation

2. Management of the work order – RCM relationship

3. Sample generation

4. Reliability analysis

Four challenges must be overcome:

Unified EXAKT Process

•Systematic•Quick•Results oriented

Typical focus

Challenge 1 Data extraction, transformation

Example: FMEA extraction

Example: Work order extraction

Ellipse input

Input from CMMS

Input from CMMS

Input from RCM Cost, RCMO, RCM Toolkit, etc

Output for LRCMOutput for LRCM Data

transformations

Data transformations

Challenge 2 LRCM …

the most difficult of the four - the key challenge

Text of the selected work order

Text of the selected knowledge record

Event type indicators: PF (blue), FF (red), S (yellow).

Add/Edit KRs (with audit trail)

“Slice and dice”

KPIs

1. Link the work orders and knowledge base.2. Build the knowledge base…

Dynamically,in the day-to-day work order process

Challenge 3: Sample generation

RCM Knoweldge base

Work Orders that have been linked to the KB

Events table (the sample)

Sample generation

Work ord. 1, FF RCMREF15

Work ord. 2, FF RCMREF16

Work ord. 3, FF RCMREF16

Work ord. 4, S RCMREF15

Work ord. 5, PF RCMREF15

CMMS Work orders Events table

EF15

B15

EF16

B16

EF16

B16

ES15

B15

EF15

B15

Sam

ple

Right (Temporary) Suspensions:

Legend:

EF: endings by failureES: endings by suspension

Life cycles:

Left Suspensions:

Cal

end

ar T

ime

/Challenge 3 cont’d:

Challenge 4: Reliability analysis and EXAKT

MaxWSDropet

th

06944.0

1781.0

27092709

781.0)(

Hazard model

+

RULE and Confidence interval

Cost model

EXAKT Decision based on:

Cost and Probability

Decision based on:Probability

RULEScatter

+

Predictive modelPredictive Model

Challenge 4 - Achieving Reliability from data in EXAKT

Age data (CMMS)CBM dataCost data

Supplied by user

Modeling Software

Intermediate results

Final Result

Cost, Availability,Profitability model

Hazard modelTransition model

RULEMaintenance

Decision

Challenge 4 - CBM+Simulation in SPAR-PHM

And plan overhaul in 6 months

No maintenance

Replace radio now

Projected worst actor following overhaul

OMDEC methodology “living reliability”

“on-the-job”IterativeIntegrated

LRCM Pilot On-the-job process Overcoming Key Challenge 2

1. Monitor work orders & KR links

2. Monitor knowledge record updates

3. Ask questions

4. Propose changes

5. Get feedback

6. Get consensus.

Team

OMDECLRCM specialists

+Company’s

Engineers, planners, supervisors, technicians

LRCM guidance

Methods,analysesmodels

Knowledge records

Work ordersand KR links

On the job teamwork

Leadership: 1. Recognition,2. Empowerment, 3. Interest

Company’sMaintenanceManagement

Progress reportsKPIs

OMDEC team participants

Murray Wiseman – LRCM, CBM specialist

Dr. Daming Lin – Maintenance data statistician and reliability expert, signal processing, reliability software, database + ETL specialist.