Presentation on theme: "Systems Prognostic Health Management April 1, 2008"— Presentation transcript:
1Systems Prognostic Health Management April 1, 2008 Systems Engineering ProgramSystems Prognostic Health Management April 1, 2008Christopher ThompsonIBM Global Business ServicesFCS LDMS ProgramDisclaimer: This briefing is unclassified and contains no proprietary information. Any views expressed by the author are his, and in no way represent those of Lockheed Martin Corporation.
2My Engineering Experience IBM Global Business Services, Dallas TXRequirements Lead/Prognostics SMEFCS Logistics Data Management Service (LDMS)Lockheed Martin Missiles and Fire Control, Dallas TXSenior Systems Engineer- Multifunction Utility/Logistics Equipment (MULE)Lockheed Martin Aeronautics, Fort Worth TXVehicle Systems - Prognostic Health Management- F-35 Joint Strike Fighter (Lightning II)Reliability Engineer- Army Tactical Missile System (TACMS)SMU School of Engineering, Dallas TX- TA for Dr. Stracener
3My Education B.S. in Electrical Engineering, SMU (1997) M.S. in Mechanical Engineering, SMU (2001)- Major: Fatigue/Fracture MechanicsM.S. in Systems Engineering (2002)- Major: Reliability, Statistical AnalysisPh.D. in Applied Science (anticipated ~ 2009)- Major: Systems Engineering (PHM)
4My DissertationFleet Based Analysis of Mission Equipment Sensor Configuration and Coverage Optimization for Systems Prognostic Health Management
5Sensor Tradeoffs As more sensors are added to your system: GOOD BAD A0 increasesLife costdecreasesweightincreasespowerincreasesP(FDI)increasesP(Prog)increasesvolumeincreasescablingincreasesMTBUMAincreasesMTTRdecreasesR(t)decreasesAUPCincreasesTRADEOFFS
6PHM Optimization Optimum AO Cost* Operational $ Availability AO LCC AUPC# of Sensors N* Other metrics will include Weight/Volume, Power (K/W), Specants (Computing Power)
7PHM Optimization structural element x x = mean distance between sensors Xx = mean distance between sensorsxstructuralelement∞ N = # of sensorsoptimumsolutionProbabilityofDetectionCrack
8For a common LRU (such as an engine), plotting engine power against against an environmental measure (such as temperature) over time:EngineInternalTemp.TimeEngine Power200% spec limit150% spec limit125% spec limit100% spec limitSevereDamageModerateDamageMildDamageNoDamage
9Estimating the damage accumulated (or life consumed) x1x2x4 (or more)150% specification limit200% specification limitSevereDamageModerateDamageMildDamage
10Hypothetical engine air/oil/fuel filter performance over its life MTBFoptimalperformanceacceptableperformancefilter performance (flow rate)distribution offailure timesengine systemdamage likelyperformancedegradedengine systemfailure likelyhazardousperformancefilter life (in miles)
11statistically significant difference Common LRU used on multiple vehicle types with platform specific (hidden) failure modesWhy is the FailureRate for the LRUin Platform 4 higher?What is differentabout Platform 4?statistically significant differenceFailureRatePlatform 1Platform 2Platform 3Platform 4
12Standard oil filter used in engines across FCS vehicles, replaced at a scheduled time/milesIncreased engine lifeconsumptionScheduledReplacementTimeCorrectactionWearout – Life Consumptionwasted filter lifeVehicle 1Vehicle 2Vehicle 3Actual Condition of the oil filter
13Time (or miles, or load cycles, or on/off cycles, …) Standard structural element across several vehicles (under cyclic loading)fleetbasedestimateRepair neededbefore estimateDamageAccumulationLRU lifehistoriesTime (or miles, or load cycles, or on/off cycles, …)
14Multifunction Utility/Logistics Equipment The MULE ProgramFuture Combat SystemsMultifunction Utility/Logistics Equipment
15Keys to the Success of FCS Reducing Logistics footprintIncreasing AvailabilityReducing Total Cost of OwnershipImplementing Performance Based LogisticsImprovements in the ‘ilities’ (RAM-T)ReliabilityAvailabilityMaintainabilityTestabilitySupportability
16Prognostics Of or relating to prediction; a sign of a future happening; a portent.The process of calculating an estimate of remaininguseful life for a component, within sufficient time torepair or replace it before failure occurs.
17Prognostic Health Management (PHM) PHM is the integrated system of sensors which:Monitors system health, status and performanceTracks system consumablesoil, batteries, filters, ammunition, fuel…Tracks system configurationsoftware versions, component life history…Isolates faults/failures to their root causesCalculates remaining life of components
18Diagnostics The identification of a fault or failure condition of an element, component, sub-system or system,combined with the deduction of the lowestmeasurable cause of that condition throughconfirmation, localization, and isolation.Confirmation is the process of validation that a failure/fault has occurred, the filtering of false alarms, and assessment of intermittent behavior.Localization is the process of restricting a failure to a subset of possible causes.Isolation is the process of identifying a specific cause of failure, down to the smallest possible ambiguity group.
19Faults and FailuresFault: A condition that reduces an element’s abilityto perform its required function at desired levels, ordegrades performance.Failure: The inability of a component, sub-systemor system to perform its intended function. Failuremay be the result of one or more faults.Failure Cascade: The result when a failure occursin a system where the successful operation of acomponent depends on a preceding component,which can a failure can trigger the failure ofsuccessive parts, and amplify the result or impact.
20Classes of Failures Design Failures: These take place due to inherent errors or flaws in the system design.Infant Mortality Failures: These cause newlymanufactured systems to fail, and can generally beattributed to errors in the manufacturing process,or poor material quality control.Random Failures: These can occur at any timeduring the entire life of a system. Electrical systemsare more likely to fail in this manner.Wear-Out Failures: As a system ages, degradationwill cause systems to fail. Mechanical systems aremore likely to fail in this manner.
21The Ultimate Goal of Prognostics The aim of Prognostics is to maximize systemavailability and life consumption while minimizingLogistical Downtime and Mean Time To Repair, bypredicting failures before they occur. This is anotional diagram indicative of a wear out failure.
22What is PHM? Prognostic Health Management (PHM) is the integrated hardware and software system which:Monitors system health, status and performanceTracks system consumablesoil, batteries, filters, ammunition, fuel…Tracks system configurationsoftware versions, component life history…Diagnoses/Isolates faults/failures to their root causesCalculates remaining life of componentsPredicts failures before they occurContinually updates predictive models with failure data
23What is PHM? Prognostic Health Management is a methodology for establishing system status and health, and projectingremaining life and future operational condition, bycomparing sensor-based operational parameters tothreshold values within knowledge base models.These PHM models utilize predictive diagnostics, faultisolation and corroboration algorithms, andknowledge of the operational history of the system,allowing users to make appropriate decisions aboutmaintenance actions based on system health,logistics and supportability concerns and operationaldemands, to optimize such characteristics asavailability or operational cost.
30Availability Analysis Availability, AchievedwhereMTBF = Mean Time Between FailureMTTR = Mean Time To Repair
31Availability Analysis Availability, OperationalwhereMTBUMA = Mean Time Between UnscheduledMaintenance ActionsALDT = Administrative Logistical Down TimeMTTR = Mean Time To Repair
32Availability Analysis MTBUMA = Mean Time Between UnscheduledMaintenance ActionswhereMTBM = Mean Time Between FailuresMTBM = Mean Time Between Maintenance
33Availability Analysis How can we improve AO?- By decreasing Administrative & Logistical Down Time (ALDT)- By increasing Mean Time Between Failures (MTBF)- By decreasing Mean Time To Repair (MTTR)- By increasing Mean Time Between Unscheduled Maintenance Actions (MTBUMA) – [by decreasing MTBR induced and MTBR no defect]
34Availability Analysis How can we decrease ALDT?- By improving LogisticsImprove scheduling of inspectionsImprove commonality of partsDecrease time to get replacements- By improving PrognosticsReplace parts before they fail, not afterMaximize use of component lifeImprove off-board prognostics trendingMore sensors!!
35Availability Analysis How can we increase MTBF?- By improving ReliabilitySelect more rugged componentsImprove life screening and testingImprove thermal management- By improving QualityBetter parts screeningBetter manufacturing processes- By adding RedundancyAt the cost of Size, Weight and Power!
36Availability Analysis How can we decrease MTTR?- By improving MaintainabilityImprove quality and efficacy trainingSimplify fault isolationDecrease number of tools and special equipmentDecrease access time (panels, connectors…)Improve Preventative Maintenance- By improving DiagnosticsImprove BIT and BITEDecrease ambiguity group sizeImprove maintenance manuals and training
37Availability Analysis How can we increase MTBM (induced/no defect)?- By improving SafetyLimit the potential for accidental damage- By improving PrognosticsImprove PHM models to monitor induced damage- By improving DiagnosticsLower the false alarm rateDon’t repair/replace things which aren’t broken!