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FRACAS: A method of analyzing the failure codes assigned to the individual work orders and identifying common themes and trends. The root cause of the high impact items are determined, with a corrective action identified and executed to prevent reoccurrence of the issue.
Copyright 2009 GPAllied©Failure Reporting, Analysis,Corrective Action SystemPresented by: Ricky Smith, CMRP
Copyright 2009 GPAllied©What is FRACAS?• A Failure Reporting, Analysis, and Corrective ActionSystem (FRACAS)– Process by which failures can be reported in a timely manner– Analyzed so that a Corrective Action System can be put in placeand eliminate or mitigate the recurrence of a failure.• The goal of FRACAS– Help an organization better understand failures– The reporting required to identify failures– Actions required to eliminate or mitigate failures
Copyright 2009 GPAllied©What is FRACAS?
Copyright 2009 GPAllied©Maintenance’s Objective“Mitigate Failures with an effective Maintenance Strategy”- Preventive Maintenance (quantitative inspection, restoration,replace, replenish, etc.)- Condition Monitoring (ID of a Failure early enough a failurecan be mitigated) also known as PdM- Run-to-Failure
Copyright 2009 GPAllied©PF CurvePriority 5 Priority 4Priority2Priority1Ultrasonic EnergyDetectedVibration AnalysisFault Detection Oil AnalysisDetectedAudible NoiseHot to TouchMechanicallyLooseAncillaryDamageFailureInitiatedConditionPRECISION PREDICTIVE PREVENTIVE RUN TO FAILURETimeCatastrophicFailureP-F CurveEquipmentCondition
Copyright 2009 GPAllied©Proactive Maintenance
Copyright 2009 GPAllied©FRACAS? Issues?“A Proactive Reliability Process is a supply chain. If a stepin the process is skipped, or performed at a substandardlevel, the process creates defects known as Failures.The output of a healthy reliability process is optimal assetreliability at optimal cost.”― Ron Thomas, former Reliability Director atDofasco Steel, Hamilton, ON
Copyright 2009 GPAllied©Causes of Failures• No repeatable procedures• Not executing PM to specification• Not performing Corrective Maintenance to specification• Personnel not trained (skills, maintenance, reliability)• Parts not stored to specifications• Not using the right tools• Best Lubrication Practices unknown• RCA is not applied or not known• No auditing of Maintenance Activity or Work
Copyright 2009 GPAllied©Known Best Practice Data• 15% of Maintenance Work is Execution of PM• 15% is the results of the PM ExecutionPM / PdM is a controlled Experiment• 15% of Maintenance is Execution of PdM• 35% is the results of PdM• Emergency Work – Less than 2%• Planned Work – 90%• Scheduled Compliance – 80% (by day by week)
Copyright 2009 GPAllied©Source: John Moubray, Nolan & HeapFailure PatternsTime TimeAge Related = 11% Random = 89%BathtubPattern A = 4%Wear OutPattern B = 2%FatiguePattern C = 5%Initial Break-in periodPattern D = 7%RandomPattern E = 14%Infant MortalityPattern F = 68%“Why is infant mortality so high?”Maybe because we do not apply the PF Curve Philosophy?
Copyright 2009 GPAllied©PF Curve with PrioritiesIdentify defect, Plan and Schedule Work Zone100%ReactiveWork
Copyright 2009 GPAllied©Work Priority DistributionPriority 1 and 2 Work is ReactivePriority 3, 4, and 5 Work is ProactivePM PdM CPM CPdM REQ
Copyright 2009 GPAllied©PF Curve and Managing Proactive MaintenancePoint PPoint F
Copyright 2009 GPAllied©Work Priority Distribution – a Few Simple RulesEMERGENT work is anythingthat is done as a Priority 1 or 2CORRECTIVE work isanything done as a result ofan inspectionCORRECTIVE work shouldnever be done as a P1 or P2ROUTINE work is anythingdone as a PRIORITY 3CORRECTIVE work can bedone as Priority 3CORRECTIVE work should bedone as a Priority 4 and 5Most CORRECTIVE workshould be done as P4 or P5 ifyou embrace P-F mentality
Copyright 2009 GPAllied©FRACAS Enables Success of an Organization
Copyright 2009 GPAllied©“It isn’t what you know that will kill you, it iswhat you don’t know that will”
Copyright 2009 GPAllied©It is all about the Data
Copyright 2009 GPAllied©How do you know where are?How do you know the Direction to reach you objective.?
Copyright 2009 GPAllied©Without good data we are lost!
Copyright 2009 GPAllied©“Bad Data” resulting in High Variation- Variation in your Maintenance Process- PM Program not effective- Variation in PM Compliance- PM program not focused on specific Failure Modes- Repeatable Corrective Maintenance Procedures notavailable or if available not used- Storeroom is a deathtrap for parts, little or no PMprogram on critical spares
Copyright 2009 GPAllied©Definition of Data Quality – ISO 14224Must have Confidence in the collected Reliability andMaintenance data, and hence any analysis, is stronglydependent on the quality of the data collected. High-qualitydata is characterized by:a) completeness of data in relation to specification;b) compliance with definitions of reliability parameters,data types and formats;c) accurate input, transfer, handling and storage ofdata (manually or electronic);d) sufficient population and adequate surveillanceperiod to give statistical confidence;e) relevance to the data users need.
Copyright 2009 GPAllied©
Copyright 2009 GPAllied©The International Standard for Failure Data
Copyright 2009 GPAllied©Failure Data – ISO 14224
Copyright 2009 GPAllied©ISO 14224 Maintenance Taxonomy Standard
Copyright 2009 GPAllied©ISO 14224 Maintenance Taxonomy Applied
Copyright 2009 GPAllied©Effects of Good Data Quality
Copyright 2009 GPAllied©KPI #1 – Asset Health ReportAsset Health Metric - The percent of assets with no identifiable “Defect”
Copyright 2009 GPAllied©KPI #2 – Mean Time Between Failure• Mean Time Between Failures (MTBF) is the average length ofoperating time between failures for an asset or component.(Definition extracted from Published SMRP Metrics)• MTBF is used primarily for repairable assets and components ofsimilar type. (Definition extracted from Published SMRP Metrics)
Copyright 2009 GPAllied©KPI #2 - MTBF Example / Reporting by TaxonomyEquipment Taxonomy (ISO 14224)Systematic classification of equipment into generic groups based on factorspossibly common to several of the itemsArea Level MTBF Component Level MTBF
Copyright 2009 GPAllied©KPI #3 – MTBF / “Equipment Condition Report”Asset Health Metric - The percent of assets with no identifiable “Defect”
Copyright 2009 GPAllied©KPI #4 – “Equipment Condition Report / Equipment Detail”Asset Health Metric - The percent of assets with no identifiable “Defect”
Copyright 2009 GPAllied©KPI #5 – Route Compliance
Copyright 2009 GPAllied©Route Compliance Impact on Asset Health ReportAsset Health Metric - The percent of assets with no identifiable “Defect”
Copyright 2009 GPAllied©KPI #6 – Monthly Maintenance Cost % of RAV• Maintenance Cost- Labor Cost- Material Cost- Contract Maintenance Cost- Overtime Cost• Correlate Cost to Other KPIs
Copyright 2009 GPAllied©Data Accuracy Requirements• Process Map• Roles and Responsibilities Identified
Copyright 2009 GPAllied©The 7 Steps to a Successful FRACAS
Copyright 2009 GPAllied©Step 1 – Determine your end goal.• The beginning of every journey starts with a destination• The beginning of the journey to an effective FRACAS is the same asany other• You must have an end goal in mind• The goal of a FRACAS is not to gather data, but to eliminate failuresfrom the organization• Knowing this helps ensure that every policy, procedure, and activity inthe system is goal oriented• The roles, goals, and responsibilities of everyone involved with thesystem can be focused toward that goal• Having the goal in mind allows you to build the shared vision andvalues that will make the system work successfully for you
Copyright 2009 GPAllied©Step 2 – Create the Data Collection Plan1. Determine the measures you will use– MTBFMean Time Between Failures (MTBF) is the average length of operating timebetween failures for an asset or component. MTBF is used primarily forrepairable assets and components of similar type.– MTTFMean Time to Failure (MTTF) is the average length of operating time to failureof a non-repairable asset or component, i.e., light bulbs, rocket engines. Arelated term, Mean Time Between Failures (MTBF), is the average length ofoperating time between failures for an asset or component. Both terms are ameasure of asset reliability and are also known as Mean Life.
Copyright 2009 GPAllied©Continued2. Determine what data needs to be collected to create the desiredmeasures
Copyright 2009 GPAllied©Continued3. Determine how the data will be collected- There are different types of data that need to be collected- Determine how the data will be collected- Failure data can be collected through the EAM/CMMS, automated processdata systems, or by using checklists4. Determine how data will be analyzed– The best bet is probably to start with Pareto– Just make sure to remember that data analysis - where to apply methodsJDI, RCA and RCM– Do not fall victim to analysis paralysis. Analytical reports are nice, but nostatistic ever solved a problem
Copyright 2009 GPAllied©Keep it simple in the beginning3rd2nd1st# of Failure byarea# of Failures byEquipment# of Failure byComponent or Part
Copyright 2009 GPAllied©Examples – Dominant Failure PatternMTBF –Object Type(Electric Motor)Cause Code=# LOL = 5%# MAERR = 10%# LOP = 20%# OPER = 65%FailurePatternCause Code=# LOL# MAERR#LOP# OPERNew Failure Pattern
Copyright 2009 GPAllied©Example Continues• % of Assets with No Identifiable Defect• Failure Rate for Specific ComponentsDrive Belt-Broken-Ageing Failure Rate0 602.49 1205 1807.5 2410 3012.5 3614.9 4217.4 4819.9 5422.4 6024.9Time00.000106960.000213930.000320890.000427850.000534810.000641780.000748740.00085570.000962660.0010696FailureRateRegionalised rateDistribution rateP0: 0%B20: 3492B15: 3208B10: 2858ε: 0.05664ρ: 0.9781γ: 0β: 3.745η: 5212Median rank2-parameterWeibull
Copyright 2009 GPAllied©Step 3 – Determine Organizational Roles, Goals,and Responsibilities (RACI)• Who collects the data?• Who analyzes the data?• Who takes what action based on analysis results?
Copyright 2009 GPAllied©Develop Process Map / Maps
Copyright 2009 GPAllied©Develop a RACI Chart for each Process MapDecisions/FunctionsMaint.ManagerMaint.SupervisorReliabilityEngineerMaint.PlannerMaint.TechFailure Data Entry I A C I RData Accuracy A R C C CRCA – Invalid Data I A R C RAnalysis of Data A I R C CActions Identified A C R I CActions Taken A R C C IFRACAS Activated A R C C I
Copyright 2009 GPAllied©Step 4 – Create the FRACAS Policies andProcedures Manual• Policies and Procedures Manual that will serve as the basis formanaging and administering the FRACAS system• This is a tedious step, but should not be skipped• This Manual will serve as the basis for developing:– Initial FRACAS Training for all key personnel– Ensuring that new employees understand and participate inFRACAS effectively– Best to develop a FRACAS Management Manual and a pocketsize book that is easy for people to carry around and refer to asrequired
Copyright 2009 GPAllied©What should be in the Manual?• Let’s develop this together to insure we both agree on the content• The manual should have at the minimum– Definition of FRACAS– Benefits of FRACAS– Managements’ Guidance– Managements’ Expectations– Roles and Responsibilities by position– FRACAS reports– Key data which must be input to generate these reports– What these reports will do for the mine– Training Outline for each person based on RACI Charts
Copyright 2009 GPAllied©Step 5 – Develop FRACAS Training Plan• Each person in the organization will need to be trained according totheir level of participation in the FRACAS• Use the Tasks Listed on all RACI ChartsDecisionFunctionMaint.ManagerMaint.SupervisorReliabilitySpecialistMaint.PlannerMaint.TechFailure Data EntryData AccuracyRCA – Invalid DataAnalysis of DataActions TakenFRACAS Activated
Copyright 2009 GPAllied©Training Plan Requirements- Training Plan should consist of the following Position : Maintenance Technician Task: Close out a Work Order Condition: Given 5 Completed Emergency Work Orders Standard: Close out Work Orders to 100% Compliance Method of Training: Lecture, Web, Reading, etc. Method of Validation: Written Test, Web Test, Verbal Recall, etc.
Copyright 2009 GPAllied©Step 6 – Implement FRACAS• Let’s turn it on– Publish FRACAS Policies and Procedures Manual– Train required personnel– Hold required informational meetings– Begin data collection on highest priority area– Analyze data and report results on Public FRACAS InformationBoard– Create corrective actions based on results.• Mitigation of Human Error• Changes to the current maintenance strategy• Changes to how production operates equipment• Resign Equipment
Copyright 2009 GPAllied©Step 7 – Monitor, Show Success, and Adjust• Monitor data quality and resultsDecisionFunctionMaint.ManagerMaint.SupervisorReliabilitySpecialistMaint.PlannerMaint.TechFailure Data Entry I A C C RData Accuracy A R C C IRCA – Invalid Data A C R I CAnalysis of Data A I R C IActions Taken A R C R IFRACAS Activated A R R C I
Copyright 2009 GPAllied©Monitor and Adjust• Good data is the backbone of good decision making• It is important to monitor data quality• Make adjustments to either the data collection plan or the trainingprogram• Insure data is consistent and informative• Many organizations believe they have good data only to find outtheir data collection is inconsistent
Copyright 2009 GPAllied©Questions?