Availability, Availability Contracting and Design for Availability

Transcription

1 Prognostics PHM Conference 2015, Contracting and Design for Peter Sandborn (301) is the ability of a service or a system to be functional when it is requested for use or operation; it is a function of the system s reliability and maintainability Operational Uptime Uptime Downtime Reliability is the probability that an item will not fail Maintainability is the probability that a failed item can be successfully restored to operation Reliability or Maintainability Uptime = accumulated uptimes (system is up and running when requested for operation) Downtime = accumulated downtimes (system is down undergoing a repair, replacement or waiting for spares, while requested for operation) 2 1

2 Simple Example 4 Hours 1 Hour 1 Hour 4 Hours Start Wait for Spares Mount Spares End Time Uptime = = 8 Hours Downtime = = 2 Hours = (8)/(8+2) = 80% 3 Standby time Operating time Uptime Uptime Downtime Logistic down time Spares availability Spares location Transportation of spares Administrative delay time Finding personnel Reviewing manuals Complying with supply procedures Locating tools Setting up test equipment Corrective maintenance time Preparation time Fault location (diagnosis) time Getting parts Correcting fault Testing Preventative maintenance time Inspection Servicing Adapted from: 4 2

3 Types of Different types of availability can be measured. These are generally, divided into the following types of measures: Time-Interval Based Measures Instantaneous (or point availability) probability that an item will be able to perform its required function at the instant it is required (e.g., Average uptime, Steady-state) Downtime Based Measures availability measures that are defined in terms of what activities are included in the downtime (e.g., Inherent, Achieved, Operational) Mission-Oriented probability that each individual failure occurring in a mission of a specific total operating time can be repaired in a time that is less than or equal to some specified time length. Materiel percentage of the total inventory of a system operationally capable (ready for tasking) or performing an assigned mission at a given time, based on materiel condition. 5 -Centric Systems Customers of critical systems with high availability requirements: Customers of avionics Large scale production lines Servers Infrastructure service providers High-volume manufacturing lines etc. Examples of a decrease of availability (consequences of failures): A machine inconvenience to customers Point-of-sale system to retail outlets huge financial loss Medical device or hospital equipment loss of life Aircraft control system loss of both life and money Military systems loss of mission, asset, and/or life Traffic lights inconvenience, economics, etc. etc. 6 3

4 Example of Impacts Nissan Smyrna Tennessee (US) Plant: Eliminating 1 minute of unplanned downtime per day would generate $17.6M of additional revenue annually Unplanned maintenance costs 67% more to resolve than planned maintenance A 30 second line pause is one less car produced 326 minutes of interrupt time (unplanned downtime) on their truck line in 2011 Target for maintenance: 70% predictive, 30% reactive (today it is reversed) Susan Brennan, VP Manufacturing Nissan Smyrna (February 29, 2012) 7 Contracts Operation and Maintenance (O&M) costs for many critical systems are driven by availability Customers of availability-centric systems are entering into availability contracts in which the customer either: - Buys the availability of the system (rather than actually purchasing the system itself) - The amount that the system developer/manufacturer is paid for is a function of the availability achieved by the customer. - Performance Based Logistics (PBL) contracts are an example People don t want to buy a quarter-inch drill. They want a quarter-inch hole. Theodore Levitt Professor and Editor of the Harvard Business Review 8 4

5 (Outcome-Based) Contracts The Armidale Class Patrol Boat Project Deliver and maintain 12 Armidale Class Patrol Boats (ACPBs) for 15 years, with a five year extension option System-level Performance-Based Logistic (PBL) contract City of Anaheim and PPM Energy Power purchase agreement (PPA) from wind farm PPM Energy is responsible for delivering the energy whether the turbines are operational or not and whether the wind blows or not The Florida Department of Transportation (FDOT) Port of Miami Tunnel Project Public Private Partnerships (PPP) FDOT will pay contractor a maximum of $32M per year for 30 years (the concession period ) If performance is 100%, Maximum Payment (MAP) is reimbursed, otherwise deductions are applied 9 Power Purchase Agreement (PPA) Modeling PPA Modeling: - An annual energy delivery target is agreed by the seller and the buyer at the beginning of the year to reflect the buyer s annual wind energy demand, which will not change through the year - Constant contract energy price applies for each MWh generated before the annual target is met - Seller still buys the energy over-delivered at an constant over-delivery energy price lower than the contract energy price - If under-delivery happens, the difference between the annual target and the amount actually delivered by wind is calculated. The seller has to buy energy to make up the difference from other sources (e.g., burning coal/oil) at a price higher than the contract energy price 10 5

6 Case Study for a Wind Farm under a PPA Cumulative Predictive maintenance revenue lost [$] Assume a 5-turbine-farm managed via a PPA, Turbines 1 & 2 indicate RULs on Day 0, Turbine 3 operates normally, Turbines 4 & 5 are down Predictive maintenance revenue lost, cost avoidance and predictive maintenance value paths for Turbines 1 & 2: Paths change slopes because annual energy delivery target (from PPA) has been reached and then a lower price applies Cost avoidance [$] Time after RUL indication [h] Time after RUL indication [h] Time after RUL indication [h] Predictive maintenance value [$] 11 Case Study for a Wind Farm under a PPA Optimum maintenance date for the turbines with RULs in a farm subject to a PPA may not be the same as individual turbines managed in isolation Expected predictive maintenance option present value [$] The optimum predictive maintenance opportunity is 2 days (48 hours) after Day 0 for the wind farm Time after RUL indication [h] Expected predictive maintenance option present value [$] Expected predictive maintenance option present value [$] The optimum predictive maintenance opportunity is 4 days (96 hours) after Day 0 if Turbine 1 is managed in isolation Time after RUL indication [h] The optimum predictive maintenance opportunity is 4 days (96 hours) after Day 0 if Turbine 2 is managed in isolation Time after RUL indication [h] 12 6

7 Design for To support availability contracts you need a design for availability capability Design for : Determine system parameters that satisfy a specific availability requirement Requirement System System s Reliability Logistics Management PHM Parameters Life Cycle Cost and ROI 13 Existing Calculations Optimization seeks to determine design parameters that maximize availability for simplified systems da A=... = 0 dx Solve for x as a function of A Only provide solution at a single selected points in time (not at all times) Implicitly assume that all uptimes are the same and all downtimes are the same (simplifying assumptions make these solutions non-applicable to real problems) Don t seek to meet a minimum, rather they attempt to maximize 14 7

8 Existing Calculations (continued) Discrete Event Simulation create timelines of a set of events and solve for availability (alternatives: Markov models and Petri nets) General, all uptimes can differ, all downtimes can differ can be evaluated at all points in time but... Discrete event simulators only run in the forward direction (increasing time) Traditionally, availability is only an output (never an input) If you want to meet an availability requirement you must iterate which becomes virtually impossible when uncertainties are introduced 15 Existing Calculations (continued) While determining the availability that results from a set of system parameters is straightforward, determining the system parameters that result in a desired availability is not 16 8

9 Objective Design for Determine system parameters that satisfy a specific availability requirement Requirement System System s Reliability Logistics Management PHM Parameters Life Cycle Cost and ROI The availability requirement could (in general) be expressed as a probability distribution The system parameters not being solved for may be uncertain 17 Parameters Affecting Either Downtime or Uptime Logistics Parameters T. Jazouli and P. Sandborn, A Design for Approach for Use with PHM, Proceedings of the International Conference on Prognostics and Health Management, Portland, OR, October

10 Design for Example DECISION - Choose an Inventory Lead Time (ILT) value to require from suppliers when ordering additional spares CONSTRAINT The system s availability should satisfy the following availability requirement: Example Inputs Set Sustainment approaches: Unscheduled Maintenance, Data-Driven PHM Subject to the availability requirement at the left TTF = Weibull Distribution; with shape parameter 1.1, scale parameter of 200, and a location parameter of 9,000 hours 19 Inventory Model Inventory Parameters: Initial Spares, Spares Threshold, Spares Replenishment, Spares Carrying Cost, Lead Time and Billing Due Date For one socket LRUs needed No spare available Inventory Downtime System is down waiting for spares Time LRUs drawn from inventory Inventory Inventory Lead Time Initial spares purchase Inventory level drops below threshold Additional spares ordered Spare replenishment 20 10

11 Design for Example Result Both of these solutions satisfy the same availability requirement This translates an availability requirement into something that you can flow down to your supply chain Unscheduled maintenance Data-driven PHM approach 21 Prognostics and Health Management Prognostics Sensors Components Interconnects Boards LRUs Wiring Systems Hazard Rate Analysis Methods Time Infant Useful life Mortality Prognostic cell Actual circuitry Wear-out Acceleration Factor (AF) Prognostics Analysis Failure of prognostic sensors Failure of actual circuit Time Predicted remaining life... if you can t quantify value or you don t have a mechanism to extract value, PHM is just an academic exercise Deriving Value Data collection and reduction Accumulating damage Determining remaining life Decision making Prognostics and Health Management Consortium 22 11

12 Design for : Resources T. Jazouli, P. Sandborn, and A. Kashani-Pour, "A Direct Method for Determining Design and Support Parameters to Meet an Requirement," International Journal of Performability Engineering, 10(2), pp , March T. Jazouli, P. Sandborn, and A. Kashani-Pour, "A Direct Method for Determining Design and Support Parameters to Meet an Requirement - Parameters Affecting Both Downtime and Uptime," International Journal of Performability Engineering, 10(6), pp , Wind Farm : G. Haddad, P.A. Sandborn, and M.G. Pecht, Using Maintenance Options to Maximize the Benefits of Prognostics for Wind Farms, Wind Energy, 17, pp , X. Lei, P.A. Sandborn, N. Goudarzi, and M.A. Bruck, PHM Based Predictive Maintenance Option Model for Offshore Wind Farm O&M Optimization, This Conference (presented on Wednesday afternoon) Prognostics and Health Management Consortium 23 Copyright 2012 CALCE 12