Ground Vehicle Reliability

Transcription

1 Ground Vehicle Design-for DoD Maintenance Symposium Orlando, Florida November 13-16, 2007 Robert M. Cranwell, Manager Center for Systems Sandia National Laboratories (SNL) Albuquerque, NM (505) Sandia Sandia is a is multiprogram a laboratory operated by Sandia Corporation, a a Lockheed Martin Company, for for the the United States Department of Energy under contract DE-AC04-94AL85000.

2 Technologies & Customer Base Design for /Maintainability Modeling & Simulation Optimization Algorithms Prognostics & Health Management Integrated Logistics Human Factors Engineering Risk Assessment/Management Sensitivity/Uncertainty Quantification Technologies Support Broad Customer Base Defense Machine Tool Farming Semiconductor Boating Health Care Petroleum Aviation Automotive Nuclear Power Coal-Fired Power Tools & Technologies Validated Through Textile

3 Supported semiconductor equipment manufacturers in improving reliability of semiconductor manufacturing equipment Results!! aup to sixfold improvement in equipment reliability! Design-for- a15%-20% reduction in life-cycle cost! aincrease % of world market share! modeling Activities included: Semiconductor Industry Optimal design trades Equipment Upgrades prediction Optimization studies Maintenance Strategies Optimal strategies Spares inventory analyses Education & Training training courses Guidelines % of World Market U.S. Japan Year Formation of

4 Why Design for? The emphasis in the new process will be design for reliability Col. William Taylor, PEO for Marine Corps land systems (in reference to the troubled Expeditionary Fighting Vehicle (EFV) program) Mr. Charles McQueary, the Pentagon s chief operational tester is advocating that the services invest more upfront to improve the reliability of new weapon systems so that the costs of maintaining them over their lifetime will be much less The investment of money in the upfront RDT&E period on meeting reliability, maintainability and availability objectives is really highly leveraged money. Charles McQueary, Operational testing should be a period of confirmation, not a period of discovery McQueary, 2007

5 should be built into products at the earliest possible stages of design % Life Cycle 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% % Locked-in 3% Concept/Feasibility Design for Supplier Cost Implications Source: Made in America, MIT Commission on Industrial Productivity Critical Design Review 12% Design/Development 95% Life Cycle Phases 35% Production/Deployment Source: Arsenault & Roberts, & Maintainability of Electronic Systems 50% Operation/Support Over 90% of all and Quality costs in the U.S. are being spent to correct product design inadequacies and defects after they have occurred, while less than 10% are being spent to make products right in the first place. Source: K.C. Kapur and L.R. Lamberson in Engineering Design

6 Design for Customer Cost Implications Notional Impact of Design for on Life Cycle Life Cycle Total Life Cycle Operational Acquisition Without Design for Total Life Cycle Operational Acquisition With Design for Life Cycle Acquisition Optimized Cost Point Operational Optimizing Life Cycle It is important to make Equipment Purchase Decisions Based on Total Life Cycle

7 Impact of Design for on LCC Planned impact of DFR on Total LLC

8 Why not more widely used? is an afterthought - Designs are committed to before reliability is considered Preoccupation with short-term profits Felt to be more costely Not supported by top management Successful Practices Design for Formalized Program (that is used throughout the life cycle) Good organized data collection program (e.g., failure recording & corrective action system) that accounts for uncertainty Careful thought given to metrics Use of predictive modeling & simulation Use of optimization techniques Emphasis on a system-level approach to design for reliability Prediction is part of the process of designing the future. MIL-HDBK 217

9 System Design Architecture Architecture Component Component Maintenance Maintenance Policies Policies Design for Engineering Cycle Step 1: System Models Model Development Predictions Sensitivity/Uncertainty Analyses Predicted Predicted System System Performance Performance Step Step 3: 3: Improvement Improvement Activities Activities No Step Step 2: 2: Design Design Meets Meets Objectives? Objectives? Yes Design Design Trade-off Trade-off Studies Studies Allocation Allocation Next Next Phase Phase Engineering Cycle activities are repeated until design meets requirements Source: by Design, AT&T

10 New Design Prediction VCE2.1 VWA2I VCE3 MT5 1 Observed vs Predicted TRANSPORT CHAMBER Cumulative Probability Projected Observed Equipment manufacturer Semiconductor manufacturing equipment New Central Wafer Handling System Considering new design based on: New technology Performance improvements Market potential Predict performance of new design Identify potential problem areas Suggest improvements Predict equipment reliability 0 System Mean Time Between Failure (MTBF) Company reporting $M s in design and life cycle cost savings from use of DFR!

11 Allocation Allocate System Requirements to Subsystems & Components Equal Apportionment Technique Using Optimization Techniques (such as GA s) Allocation of System Requirements The ARINC Apportionment Technique The AGREE Allocation Method Effort Minimization Algorithm Using Dynamic Programming Shows where to optimally spend your dollars

12 Standard Approach: Resources not guided by allocation process Resource Allocation Part LRU 1 LRU 2 LRU 3 LRU 4 Funding for Redesign $1.6M $1.6M $1.6M $1.6M Does not easily account for differing costs required to upgrade parts Difficult to apply for multiple objectives (cost and readiness) Does not support iterative process needed to reach optimal allocation Optimal Approach Updated Part LRU 1 LRU 2 LRU 3 LRU 4 Allocated Improvement Level Effort to Improve Easy Int. Hard Very Hard Cost vs. Improvement Current Part LRU 1 LRU 2 LRU 3 LRU 4 Funding for Redesign $0.5M $1.6M $0.2M $2.7M Perform Perform optimal optimal reliability reliability allocation allocation Design Design upgrades to upgrades to reach reach target performance target performance Estimate Estimate costs costs Iterative process as design efforts progress Iterative Redesign Evaluation Allocation process provides efficient targeting of of resources

13 Resource Allocation Optimization Objectives: Improve system availability 20% Improvement options: Improve reliability of major subsystems and components For example: Level 0 = no improvement Level 1 = 20% improvement Level 2 = 40% improvement Level 3 = 60% improvement Level 4 = 80% improvement Level 5 = 100% improvement Constraint: to improve failure rates of cost & availability drivers Find optimal allocation (level of improvement) of each subsystem and component given costs constraints The results show where to to optimally spend your dollars

14 Mounted Combat System (MCS) Importance of Simulation in DFR FCS System of Systems (SoS) Force Structure Manned Ground Vehicles (MGV) Infantry Carrier Vehicle (ICV) Command and Control Vehicle (C2V) Unmanned Aerial Systems (UAS) Class I UAV Class IV UAV Reconnaissance And Surveillance Vehicle (RSV) Unattended Ground Systems (UGS) T-UGS U-UGS Common Chassis on-line of ight Cannon NLOS-C) Non-Line of Sight Mortar (NLOS-M) Medical Vehicle Treatment (MV-T) MULE-C Tactical and Urban Unattended Ground Sensors Unmanned Ground Vehicles (UGV) Multifunction Utility/ Logistics and Equipment Countermine and Transport MULE-T Non-Line of Sight Launch System (NLOS-LS) Small UGV (SUGV) FCS Recovery and Maintenance Vehicle (FRMV) Armed Robotic Vehicle Assault (Light) (ARV-A-L) Medical Vehicle Evacuation (MV-E) 6 Feb 07

15 Simulation of FBCT Combat Mission SoS Simulated Mission Utilizes: Time-Dependent Operational Status SoS MOE s/tpm s (e.g., A 0 ) Sensitivity Trades Integrated Logistics Status Optimization Analyses... Integrated methodology provides unique capabilities for SoS analysis Unique State Modeling Concepts State-of-Art SoS Simulation Framework Customized Optimization Algorithms Provides Capability for Analyzing: Systems as well as Systems-of-Systems Warfight capabilities (pre, post, during campaigns) Spiral integration of technologies Flexibility to Include: Complex supply & communication networks Stochastic treatment of combat damage Human performance (maintainers, warfighters, ) Allows Analyst to Assess Impacts of: PHM Asset visibility/reduced consumables Parts on board/crew repairable