Reliability Prediction - A UK MoD Position
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1 Reliability Prediction - A UK MoD Position Matthew Knight Land Battlespace Systems Dept. Fort Halstead mjknight@dstl.gov.uk
2 Session Overview Traditional Approach Defence Standard The New Era New Techniques Conclusion
3 Why Bother? To provide an insight into a Systems Reliability Reliability Requirements are written into the procurement contracts SR The system shall have at least a 96% Probability of completing a Battlefield Day (War fighting) Mission without a Mission Failure with 80% Confidence
4 The Traditional Approach Defence Standard series Reliability & Maintainability R&M Predictions ; (1993) Similar Equipment Extrapolation Generic Parts
5 Reliability Prediction Methods (1) To support the R&M Case; part 3 (2003) A Readable overview of a contractor s approach and the framework in which evidence can be placed to provide progressive assurance that a system is achieving it s specified R&M requirements. (Reference may be made, as appropriate, to lower documents, results of trials and detailed analysis)
6 Hopefully avoiding undesirable situations
7 Reliability Prediction Methods (2) Def-Stan R&M predictions are imprecise uncertainty in transferring failure data to different applications, environments etc; ambiguity on what constitutes a "failure" in different situations the effect of human actions, e.g. in manufacturing, quality control, maintenance, diagnosis, etc No. 1 - Similar Equipment Method (in-service data) No. 2 - Extrapolation of Tests & Trials Data No. 3 - Generic Parts Method
8 The New Era Def-Stan cancelled (2006) Smart Procurement Faster, Cheaper, Better Defense Industrial Strategy (Nov. 2005) DE&S To improve how military equipment, supplies & services are procured & supported Contracting for Availability Contractor Logistics Support COTS
9 New Requirements A diminishing responsibility for support, not directly concerned with Reliability & Maintainability Availability Requirements, UR 251: The User shall be able to sustain a deployed system capability, with a mean Operational Availability of 95% (threshold) over 14 days high intensity combat (with a minimum Operational Availability of 88% Requires a significant change in the way that R&M evidence will be gathered and reviewed in the future
10 Initiatives to support Smart Procurement Availability Modeling - MILO Reliability Prediction of COTS BBN s Life Assessment during design through Physics of Failure Modeling Automatic Data Collection and Prognostics
11 MILO - Model of Integrated Logistics Operations A Land Systems simulation tool for assessing Operational Availability A means of testing new logistic solutions to support the Rapid Reaction doctrine of current defence thinking An event driven model covering Supply & Distribution, Equipment Support and Logistic Command & Control - concurrently.
12 MILO - Supply & Distribution Consumption regular surge event driven Stock demand Prioritises Part supply and retain issue for later Support Commanders Main Effort Stock Handling Transport Management
13 MILO - Equipment Support Creates casualty from MTBF of MDBF Recovers vehicles Workshop and roadside repair Time to repair (MTTR) Returns to unit Consumes Spares Recovery Vehicle Failures
14 Some notes on Operational Availability (Ao)
15 MILO - Availability Uptime MTBF Inherent Availability = = Uptime + Downtime MTBF + MTTR Operational Uptime Availability = = Uptime + Downtime MTBF + MTBF ( MART + ALDT ) Where Downtime for Ao = MART + ALDT and Administrative & Logistic Delay Time (ALDT) consists of: Waiting for failure diagnosis Waiting for spares Waiting for resources (manpower or facilities) Recovery of the platform from break down area Workshop queuing Return of the platform to unit
16
17 MILO - Output Examples
18 MILO - Effect on Availability
19 Reliability Prediction - COTS What R&M evidence is available? Is the evidence provided relevant to; environment specification (similar?) Failure definition mission/basic Reliability Prediction using Bayesian Belief Networks - TRACS
20 TRACS & BBN s Transport Reliability Assessment & Calculation System Uses Bayseian Belief Networks (BBN s) to ; Use historical objective data to predict likely reliability of future vehicles; Combines statistical modelling with expert judgements represented by probabilities Model expertise about manufacturing and design capability Deals with uncertainty can make predictions with missing/unknown data; Perform what-if? and sensitivity analysis;
21 Bayesian Technology Provides a powerful framework in which to reason about uncertainty using diverse forms of evidence Evidence can be entered and retracted, with immediate impact on the predictions being produced by the BBN. All evidence entered by the assessor are exposed and visible to all.
22 Bayes Theorem Bayesian Belief networks are based on Bayes Theorem developed by the Reverend Thomas Bayes Bayes developed his Theory of Probability An Essay towards solving a problem in the doctrine of chances published the Royal Society of London in 1764 Thomas Bayes
23 Bayes Theorem in Action (1) Bayes Theorem operates by updating our beliefs based on historical experience using available data; For example, we might wish to predict the chance of rain based on observing a cloudy sky, knowing that it doesn t always rain when it is cloudy. Probability (Cloudy Rain ) p(a B) = p(b A) p(a) p(b) Probability (Rain Cloudy) Probability (Rain ) Probability (Cloudy )
24 Bayes Theorem in Action (2) We can enter our historical experience into Bayes theorem as follows: Probability (Cloudy Rain ) = 1 p(a B) = p(b A) p(a) p(b) Probability (Rain ) = 0.1 = 0.33 Probability (Rain Cloudy) Probability (Cloudy ) = 0.3 Observing a cloudy sky increases our belief that it will rain from 10% to 33%!
25 TRACS in Action (1) A vehicle is constructed in TRACS by selecting subsystems from an editable sub-system library built into the TRACS database Vehicle nominal reliability Modular Construction Wheels Engine Front Axle Transmission Rear axle
26 TRACS Process Flow Diagram In-Service Reliability Estimate Production Reliability Estimate Prototype Reliability Estimate Manufacturing Capability Assessment Nominal Reliability Design Capability Assessment Sub-system Sub-system Sub-system
27 TRACS in Action (2) Reporting Results
28 TRACS in Action (3) BFA (retrospective) Good correlation between Prediction & results DROPS (retrospective) GPMC (new) Combat Support Vehicle
29 Conclusions MoD is shifting emphasis from R&M to Availability New assessment tools to formulate Availability Requirements New techniques to assess COTS reliability
30 Finally..Beware of a false sense of security
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