Requirements Flowdown for Prognostics and Health Management

Prognostics and Health Management (PHM) principles have considerable promise to change the game of lifecycle cost of engineering systems at high safety levels by providing a reliable estimate of future system states. This estimate is a key for planning and decision making in an operational setting. While technology solutions have made considerable advances, the tie-in into the systems engineering process is lagging behind, which delays fielding of PHM-enabled systems. The derivation of specifications from high level requirements for algorithm performance to ensure quality predictions is not well developed. From an engineering perspective some key parameters driving the requirements for prognostics performance include: (1) maximum allowable Probability of Failure (PoF) of the prognostic system to bound the risk of losing an asset, (2) tolerable limits on proactive maintenance to minimize missed opportunity of asset usage, (3) lead time to specify the amount of advanced warning needed for actionable decisions, and (4) required confidence to specify when prognosis is sufficiently good to be used. This paper takes a systems engineering view towards the requirements specification process and presents a method for the flowdown process. A case study based on an electric Unmanned Aerial Vehicle (e-UAV) scenario demonstrates how top level requirements for performance, cost, and safety flow down to the health management level and specify quantitative requirements for prognostic algorithm performance.

[1]  Frank L. Greitzer,et al.  Embedded Prognostics Health Monitoring , 2002 .

[2]  P. Sandborn,et al.  The analysis of Return on Investment for PHM applied to electronic systems , 2008, 2008 International Conference on Prognostics and Health Management.

[3]  Christopher B. Ligetti,et al.  Cost-Benefit Analysis Trade-Space Tool as a Design-Aid for the U.S. Army Vehicle Health Management System (VHMS) Program , 2009 .

[4]  Michael A Ashby,et al.  An approach for conducting a cost benefit analysis of aircraft engine prognostics and health management functions , 2002, Proceedings, IEEE Aerospace Conference.

[5]  I. D. Walker,et al.  A novel approach to robotic climbing using continuum appendages in in-situ exploration , 2012, 2012 IEEE Aerospace Conference.

[6]  I. Turner,et al.  On Quantifying Cost-Benefit of ISHM in Aerospace Systems , 2007, 2007 IEEE Aerospace Conference.

[7]  David Stark.  Prognostics and Health Management (PHM) , 2010 .

[8]  D.L. Goodman,et al.  Return-on-investment (ROI) for electronic prognostics in mil/aero systems , 2005, IEEE Autotestcon, 2005..

[9]  H. Hecht,et al.  Prognostics for electronic equipment: an economic perspective , 2006, RAMS '06. Annual Reliability and Maintainability Symposium, 2006..

[10]  K. Goebel,et al.  Standardizing research methods for prognostics , 2008, 2008 International Conference on Prognostics and Health Management.

[11]  Peter Sandborn,et al.  A Methodology for Determining the Return on Investment Associated With Prognostics and Health Management , 2009, IEEE Transactions on Reliability.

[12]  Richard C. Millar,et al.  Defining requirements for advanced PHM technologies for optimal reliability centered maintenance , 2009, 2009 IEEE Aerospace conference.

[13]  Sankalita Saha,et al.  Evaluating algorithm performance metrics tailored for prognostics , 2009, 2009 IEEE Aerospace conference.

[14]  Chris Drummond,et al.  Reverse-Engineering Costs : How much will a Prognostic Algorithm save ? , 2008 .

[15]  R. Smith,et al.  A process and tool for determining the cost/benefit of prognostic applications , 2001, 2001 IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference. (Cat. No.01CH37237).

[16]  Sankalita Saha,et al.  Metrics for Offline Evaluation of Prognostic Performance , 2021, International Journal of Prognostics and Health Management.

[17]  Michael J. Roemer,et al.  Extending FMECA-health management design optimization for aerospace applications , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[18]  K. Goebel,et al.  Diagnostic information fusion: requirements flowdown and interface issues , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[19]  Johan Reimann,et al.  Using Condition Based Maintenance to Improve the Profitability of Performance Based Logistic Contracts , 2009 .

[20]  J. Banks,et al.  Cost Benefit Analysis for Asset Health Management Technology , 2007, 2007 Annual Reliability and Maintainability Symposium.

[21]  J. Kurien,et al.  Costs and Benefits of Model-based Diagnosis , 2008, 2008 IEEE Aerospace Conference.

[22]  Michael J. Roemer,et al.  Health management system design: Development, simulation and cost/benefit optimization , 2002, Proceedings, IEEE Aerospace Conference.

[23]  B.P. Leao,et al.  Cost-Benefit Analysis Methodology for PHM Applied to Legacy Commercial Aircraft , 2008, 2008 IEEE Aerospace Conference.

[24]  T. Yoneyama,et al.  Prognostics performance metrics and their relation to requirements, design, verification and cost-benefit , 2008, 2008 International Conference on Prognostics and Health Management.

[25]  Andrew Hess,et al.  Writing a convincing cost benefit analysis to substantiate autonomic logistics , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[26]  Chunsheng Yang,et al.  Model evaluation for prognostics: estimating cost saving for the end users , 2007, Sixth International Conference on Machine Learning and Applications (ICMLA 2007).

[27]  Hefin Rowlands,et al.  Quality function deployment: the unused tool , 2000 .

[28]  Liang Tang,et al.  A Novel RSPF Approach to Prediction of High-Risk, Low-Probability Failure Events , 2009 .

[29]  Kimon P. Valavanis,et al.  On Integrating Unmanned Aircraft Systems into the National Airspace System: Issues, Challenges, Operational Restrictions, Certification, and ... and Automation Science and Engineering) , 2008 .

[30]  A. Khalak,et al.  Influence of prognostic health management on logistic supply chain , 2006, 2006 American Control Conference.

[31]  Sankalita Saha,et al.  Requirements Specification for Prognostics Performance - An Overview , 2010 .

[32]  Benoît Iung,et al.  PROGNOSTIC DESIGN: REQUIREMENTS AND TOOLS , 2009 .

[33]  J. Luna,et al.  Metrics , Models , and Scenarios for Evaluating PHM Effects on Logistics Support Joel , 2009 .

[34]  Kimon P. Valavanis,et al.  UAS Safety Assessment and Functional Requirements , 2012 .

[35]  Bhaskar Saha,et al.  Battery health management system for electric UAVs , 2011, 2011 Aerospace Conference.

[36]  Wei Chen,et al.  Cost-Benefit Quantification of ISHM in Aerospace Systems , 2007 .

[37]  K. Krithivasan,et al.  Learning Algorithms for Grammars of Variable Arity Trees , 2007, ICMLA 2007.

[38]  M.J. Roemer,et al.  Prognostic enhancements to gas turbine diagnostic systems , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[39]  Scott Poll,et al.  A Survey of Health Management User Objectives Related to Diagnostic and Prognostic Metrics , 2009 .

[40]  K. Goebel,et al.  Metrics for evaluating performance of prognostic techniques , 2008, 2008 International Conference on Prognostics and Health Management.

[41]  M. Glade,et al.  Life cycle cost impact of using prognostic health management (PHM) for helicopter avionics , 2007, Microelectron. Reliab..

[42]  Bhaskar Saha,et al.  Model Adaptation for Prognostics in a Particle Filtering Framework , 2011 .

[43]  Ping Xu,et al.  Prognostics and Health Management (PHM) system requirements and validation , 2010, 2010 Prognostics and System Health Management Conference.

[44]  Michael J. Roemer,et al.  A Prognostic Modeling Approach for Predicting Recurring Maintenance for Shipboard Propulsion Systems , 2001 .

[45]  Michael Pecht,et al.  Prognostics and Health Management , 2013 .

[46]  Irem Y. Tumer,et al.  A functional failure reasoning methodology for evaluation of conceptual system architectures , 2010 .