Pablo Garcia Sandia JSF PM Sandia National Laboratories (SNL) Albuquerque, NM 87185 Phone: (505)844-5799 Fax: (505)84

1. Pablo Garcia Sandia JSF PM Sandia National Laboratories (SNL) Albuquerque, NM 87185 Phone: (505)844-5799 Fax: (505)844-3321 Email: pgarcia@sandia.gov Web Site: reliability.sandia.gov Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company,for the United States Department of Energy under contract DE-AC04-94AL85000.

2. Prognostics & Health Management (PHM)  Conditioned-Based Maintenance (CBM) Total Productive Maintenance System Availability Reliability Centered Maintenance Computerized Maintenance Management Systems (CMMS) Preventive Maintenance Inspection Run to failure 1930 1950 2000 1990 Evolving Maintenance Strategies “The function of maintenance in a world-class operating environment is not to simply maintain, but to provide reliable systems and to extend the life of systems at optimum costs.” Stanley Lasday, June 1997, Industrial Heating

3. Why PHM? The High Cost of Maintenance • Estimated that U.S. industry needlessly squanders in excess of $200 billion each year on inadequate or unnecessary maintenance procedures. • One major company has stated that maintenance was its “single largest controllable cost opportunity representing $100-300 million per year corporate wide.” • DoD spends in excess of $85B/yr on maintenance & support activities • Universally, Industry & government have indicated need for better, more robust “predictive maintenance” capabilities.

4. System Health Condition Functioning Degrading Failing Time Associated Cost with Time of Replacement $$$ Prescriptive replacement of functioning “good” item $$$$$$$ Lose of life and/or system due to catastrophic failure $ Optimum Replace item with maximum usage before failure Prognostics & Health Management Definition: The capability to estimate the likelihood of a system failure over some future time interval so that appropriate actions can be taken. A Prognostics & Health Management (PHM) system consists of: • Raw Data • Sensor data • Historical maintenance/failure data • Diagnostics • Data fusion • Data interpretation • Prognostics • Predictions • System “health” • Health Management • What should be done • When should it be done

5. PHM Systems • PROBLEM: Prognostics and Health Management (PHM) systems must be highly customized to the specific equipment being monitored. • OUR GOAL is to develop a robust, general prognostics capability that can be applied to many situations. • OUR APPROACH is to develop a toolkit of reusable software objects and components and an architecture for their use.

6. Categorization of Failure Modes Possible Failure Modes of System • Some – Can predict well with sensors (~10%-20%) • Some – Candidates for sensors, but technology not ready • Some – May or may not be candidates for sensors, but have good time-to-failure data • Some – Not candidates for sensors and have poor life data • Some – Random

7. MEMS Sensor Array Pressure Flow Temperature Tissue PH Oxygen Tension Orientation MEMS Technology Memory CPU Power Generator Communication Coil Valves PDP = Positive displacement pump A3 A4 A5 MEMS Actuators Wireless, Self-Powered MEMS Sensor & Actuator Void Growth Analysis Corrosion Analysis Materials Aging SNL Technologies Support PHM Technologies for SNL’s NW’s Enhanced Surveillance & Life Extension Programs • Modeling & Simulation • Predictive analyses • Sensitivity/Uncertainty analyses • Optimization analyses • Microsensor Development • Fiber-optic chemical sensors • Integrated/Micromachine Sensors • Micromachine Ion Mobility Spectrometer • Wireless, self-powered sensors • Life Prediction Algorithms • Fuzzy Logic • Neural networks, Bayesian networks • Genetic Algorithms • Bayesian updating • Electronics Quality/Reliability Center • Failure analysis • Burn-in elimination & testing • Life prediction • Materials Aging • Life extension • Reliability predictions

8. Power Plant PHM Manufacturing Facility PHM Machine Tool PHM F-16 ADG PHM AH-64 PHM Automotive PHM ABL PHM SNL PHM Activities/Programs • Nuclear Power Plant “Smart” Equipment • DOE Nuclear Energy Research Initiative (NERI) • Introduce PHM to selected power plant equipment • Manufacturing Facility PHM • DOE funded program • Implement PHM in manufacturing facility • Machine Tool PHM • DOE funded program • Implement PHM on SNL machine tools • F-16 Accessory Drive Gearbox (ADG) • Joint Shared Vision program with LM Aero • Extend replacement intervals • AH-64 Apache Attack Helicopter • Program with Apache PO • Utilize data from Flight Data Recorders • Automotive Industry On-Board Diagnostics for Emissions • Robust algorithm development for emissions control • Pattern recognition and decision algorithms • Airborne Laser PHM

9. Evidence Engine Consequence Engine System Health Maintenance Scenarios Component Health Consequence Analysis Raw Sensor Data Optimal Maintenance Recommendations Failure Mode Health Sensor Feature Interpretation (SPC, EWMA, NN) Sensor Feature Extraction SNL PHM System Architecture Updated TTF Distributions Estimates of Remaining Useful Life • Environmental Conditions • Maintenance History • Aging and Time-to-Failure System Model Data Fusion: Bayesian Belief Networks

10. x predicted service life Consequence Engine Concept PREDICTED SERVICE LIFE What did they do ? (predicted loads) and How long did they do it ? (exposure) predicted loads predicted loads Loads under normal operating conditions Flight loads predicted loads predicted loads profileB profile C profile A profile D predicted exposure predicted exposure predicted exposure predicted exposure x’ normal service life Service Life (in hours) Confidential and Proprietary

11. AlliedSignal Flexible Manufacturing System (FMS) PHM Clients Real Time Sensor Data Data Server Machine Repair Data • Monitoring multiple machines at facility • Data Server filters & stores real-time sensor data • Interfaces with CMMS • CMMS stores all historical maintenance data • Networked for remote access CMMS Factory Floor PHM

12. PHM Future Research Plans • Evidence Engine • Add more signal processing algorithms • Add a case-based reasoning capability • Determine how sensor data (such as accelerometer data tracking vibration) can be correlated with flight recorder data to detect fault conditions vs. harsh operating environments • Develop algorithms to incorporate inspection and maintenance data to update component Time-To-Failure • Consequence Engine • Add capability to allow redundancy in the underlying model • Enhance capability to update aging factors based on mission profile (referred to as the “variable loading” problem) • - Add capability to generate scenarios automatically and optimize on them (currently the consequence engine analyzes the scenario that the user inputs)

13. PHM Future Research Plans A Systems Driven, Physics-Based Approach for PHM of Electronic Systems – Proposed Research Problem: • Electronic failures appear random & unpredictable • Predicting such failures requires an understanding of underlying failure processes that does not exist • Research into failure processes is usually reactive, and proactive research into failure processes often not focused Solution: • Utilize SNL’s strengths in materials aging modeling to gain an understanding of process behind “random” failures • Use a systems approach (a la system reliability optimization) to direct model development towards the most promising areas • Obtain physics-based underpinning on which a PHM capability for electronic systems can be built

14. Backup slides

15. Sensor Feature Extraction/Fusion • Sensor feature extraction: extract key information from high and low-bandwidth sensor data • Time synchronous averaging • Kurtosis and other moments • Level shifts • Peaks ... • Sensor fusion: Combine sensor features to draw conclusions • Data fusion algorithms • Bayesian Belief Networks, Neural Networks • Case-based and model-based reasoning

16. Evidence Engine • Integrates information from a variety of sources in order to take into account all the evidence that impacts a prognosis for system health: • Maintenance history • Time to Failure distributions • Aging data and algorithms • Environmental conditions. • Uses a Bayesian Belief Network (BBN) for information fusion • Additional prognostic algorithms are used to detect trends, abstract and identify sensor features, estimate remaining useful life, etc.

17. Consequence Engine • Purpose: Most prognostic systems focus solely on predicting remaining life. The Consequence Engine takes the predictions and simulates how various maintenance actions affect system level decisions in terms of cost, availability, mission effectiveness, etc. • Consequence Engine includes the following: • Database of scheduled activities • Discrete-event simulation capability • simulate failures, maintenance actions, downtime, etc. • calculate system performance measures such as Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR), Time to Failure (TTF), etc. • Capability to allow for different operational states (e.g., partially mission capable vs. up or down).

18. Virtual System System Model Simulated Failure and Repair Events Simulated Sensor Data Virtual System Simulated Mission Profile Performance Metrics: Mission Completion Prob. Maintenance Cost Downtime Availability Parts Requirements Scenario Generation Planned Use Schedule Virtual System Simulator A software tool that simulates the behavior of a system including failures, maintenance, and sensor signals • Motivation • Real system experiences failures and repairs too infrequently to support PHM • Require capability for high-speed, realistic, reliability simulation of system to be monitored • - Support PHM design & testing • - Provide platform for realistic demos • - Analyze consequences of maintenance decisions

19. f(t) Updated TTF Distribution Original TTF Distribution Current Time 2000 Time to Failure (hours) PHM Goal: Update TTF Distribution Note: The updated TTF Distribution may be based on a more severe mission profile, sensor indications, inspection results, etc. or a combination of these.