A new life system approach to the Prognostic and Health Management (PHM) with survival analysis, dynamic hybrid fault models, evolutionary game theory, and three-layer survivability analysis

In this paper, I propose a new architecture for PHM, which is characterized by life-system approach— treating PHM as a hierarchical system with fundamental properties similar to those of life systems. Conceptually, besides drawing on the important concepts from existing PHM theory and practice such as life cycle, condition-based maintenance (CBM), remaining useful lifetime (RUL), I draw on the dynamic hybrid fault models (DHF) from fault tolerance theory and agreement algorithms, three-layer survivability analysis from survivable network systems (SNS), population dynamics from population ecology, and survival analysis from biostatistics and biomedicine. Methodologically, three main mathematical tools: survival analysis (including competing risks analysis and multivariate survival analysis), dynamic hybrid fault models and evolutionary game theory (EGT) are applied for PHM modeling and analysis. Operationally, the three-layer survivability analysis is applied to deal with the so-called UUUR(Unpredictable, latent, Unobserved or Unobservable Risks) events and to achieve sound decision-making. Overall, the advantages of the new architecture include: (1) Offer a flexible architecture that is not only compatible with existing components/approaches of PHM, such as lifetime, reliability, maintainability, safety, data-driven prognostics, and model-based prognostics, but also readily extendable to incorporate fault tolerance, survivability, and security. (2) Utilize survival analysis, competing risks analysis, and multivariate survival analysis for better modeling of lifetime and reliability at both individual and population (group of components) levels. (3) Approach the fault tolerance and reliability of the monitoring sensor network in PHM and those of the underlying physical system with the DHF models. (4) Analyze the system survivability (sustainability) with the three-layer survivability analysis approaches. (5) Capture security events with UUUR events and incorporate security policies into PHM.

[1]  J.H. MacConnell,et al.  ISHM & Design: A review of the benefits of the ideal ISHM system , 2007, 2007 IEEE Aerospace Conference.

[2]  Axel Krings,et al.  New approaches to reliability and survivability with survival analysis, dynamic hybrid fault models, and evolutionary game theory , 2008 .

[3]  Mark Kot,et al.  Elements of Mathematical Ecology: Frontmatter , 2001 .

[4]  Nancy R. Mead,et al.  Survivable Network Systems: An Emerging Discipline , 1997 .

[5]  Su-Chun Cheng,et al.  Semiparametric regression analysis of mean residual life with censored survival data , 2005 .

[6]  M. Crowder Classical Competing Risks , 2001 .

[7]  R.C. Millar A Systems Engineering Approach to PHM for Military Aircraft Propulsion Systems , 2007, 2007 IEEE Aerospace Conference.

[8]  W. R. Buckland Theory of Competing Risks , 1978 .

[9]  R. Friend,et al.  A testbed for data fusion for helicopter diagnostics and prognostics , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[10]  S. Ofsthun,et al.  Health Management Engineering Environment and Open Integration Platform , 2007, 2007 IEEE Aerospace Conference.

[11]  Philip Hougaard,et al.  Analysis of Multivariate Survival Data , 2001 .

[12]  Gordon Johnston,et al.  Statistical Models and Methods for Lifetime Data , 2003, Technometrics.

[13]  T. Dabney,et al.  PHM a key enabler for the JSF autonomic logistics support concept , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[14]  J.R. Bock,et al.  Ontogenetic reasoning system for autonomic logistics , 2005, 2005 IEEE Aerospace Conference.

[15]  A.W. Krings,et al.  Fault-Models in Wireless Communication: Towards Survivable Ad Hoc Networks , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[16]  M. Nowak Evolutionary Dynamics: Exploring the Equations of Life , 2006 .

[17]  A. Hess,et al.  The Joint Strike Fighter (JSF) PHM concept: Potential impact on aging aircraft problems , 2002, Proceedings, IEEE Aerospace Conference.

[18]  Emmanuel Adamides,et al.  Model-based assessment of military aircraft engine maintenance systems , 2004, J. Oper. Res. Soc..

[19]  E.R. Brown,et al.  Prognostics and Health Management A Data-Driven Approach to Supporting the F-35 Lightning II , 2007, 2007 IEEE Aerospace Conference.

[20]  J. Cook,et al.  Reducing Military Helicopter Maintenance Through Prognostics , 2007, 2007 IEEE Aerospace Conference.

[21]  Torben Martinussen,et al.  Dynamic Regression Models for Survival Data , 2006 .

[22]  Axel W. Krings,et al.  Insect sensory systems inspired computing and communications , 2009, Ad Hoc Networks.

[23]  Gregory Levitin,et al.  Multi-State System Reliability - Assessment, Optimization and Applications , 2003, Series on Quality, Reliability and Engineering Statistics.

[24]  Axel W. Krings,et al.  Dynamic populations in genetic algorithms , 2008, SAC '08.

[25]  Leslie Lamport,et al.  The Byzantine Generals Problem , 1982, TOPL.

[26]  C.S. Byington,et al.  Automated Health Management for Gas Turbine Engine Accessory System Components , 2008, 2008 IEEE Aerospace Conference.

[28]  R.C. Millar The Role of Reliability Data Bases in Deploying CBM+, RCM and PHM with TLCSM , 2008, 2008 IEEE Aerospace Conference.

[29]  Meike J. Wittmann,et al.  Mathematical Ecology , 2006 .

[30]  J.H. MacConnell Structural Health Management and Structural Design: An Unbridgeable Gap? , 2008, 2008 IEEE Aerospace Conference.

[31]  I. Olkin,et al.  A Multivariate Exponential Distribution , 1967 .

[32]  J. Klein,et al.  Survival Analysis: Techniques for Censored and Truncated Data , 1997 .

[33]  Tamraparni Dasu,et al.  A note on residual life , 1990 .

[34]  Z. Ma,et al.  A survival-analysis-based simulation model for Russian wheat aphid population dynamics , 2008 .

[35]  C.S. Byington,et al.  Layered classification for improved diagnostic isolation in drivetrain components , 2006, 2006 IEEE Aerospace Conference.

[36]  J.W. Bird,et al.  Propulsion System Prognostics R&D Through the Technical Cooperation Program , 2007, 2007 IEEE Aerospace Conference.

[37]  J. Cook,et al.  Contrasting approaches to the validation of helicopter HUMS - a military user's perspective , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[38]  S. Uckun,et al.  Model Based IVHM System for the Solid Rocket Booster , 2008, 2008 IEEE Aerospace Conference.

[39]  Zhanshan Ma,et al.  Multivariate Survival Analysis (I): Shared Frailty Approaches to Reliability and Dependence Modeling , 2008, 2008 IEEE Aerospace Conference.

[40]  Thomas A. Mazzuchi,et al.  The proportional hazards model in reliability , 1989, Proceedings., Annual Reliability and Maintainability Symposium.

[41]  Richard C. Millar,et al.  A Survey of Advanced Methods for Analysis and Modeling of Propulsion System Reliability , 2007 .

[42]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data: Kalbfleisch/The Statistical , 2002 .

[43]  Rui Kang,et al.  China's Efforts in Prognostics and Health Management , 2008, IEEE Transactions on Components and Packaging Technologies.

[44]  D.,et al.  Regression Models and Life-Tables , 2022 .

[45]  D. Wroblewski,et al.  A testbed for data fusion for engine diagnostics and prognostics , 2002, Proceedings, IEEE Aerospace Conference.

[46]  J. Fletcher Evolutionary Game Theory, Natural Selection, and Darwinian Dynamics , 2006, Journal of Mammalian Evolution.

[47]  Axel W. Krings,et al.  Multivariate Survival Analysis (II): An Overview of Multi-State Models in Biomedicine and Engineering Reliability , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[48]  J.K. Line,et al.  Electronic Prognostics Through Advanced Modeling Techniques , 2007, 2007 IEEE Aerospace Conference.

[49]  Zhanshan Ma,et al.  Survival Analysis Approach to Reliability, Survivability and Prognostics and Health Management (PHM) , 2008, 2008 IEEE Aerospace Conference.

[50]  K. Keller,et al.  Power Conversion Prognostic Controller Implementation for Aeronautical Motor Drives , 2008, 2008 IEEE Aerospace Conference.

[51]  P.W. Kalgren,et al.  Defining PHM, A Lexical Evolution of Maintenance and Logistics , 2006, 2006 IEEE Autotestcon.

[52]  A.W. Krings,et al.  Bio-Robustness and Fault Tolerance: A New Perspective on Reliable, Survivable and Evolvable Network Systems , 2008, 2008 IEEE Aerospace Conference.

[53]  D Commenges,et al.  Multi-state Models in Epidemiology , 1999, Lifetime data analysis.

[54]  J. Gamarra,et al.  Metapopulation Ecology , 2007 .

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

[56]  Tianbiao Yu,et al.  Research on prognostic health management (PHM) model for fighter planes based on flight data , 2008, 2008 International Conference on Condition Monitoring and Diagnosis.

[57]  Yun Liu and Kishore S. Trivedi,et al.  Survivability Quantification: The Analytical Modeling Approach , 2006 .

[58]  Joseph G. Ibrahim,et al.  Bayesian Survival Analysis , 2004 .

[59]  Josef Hofbauer,et al.  Evolutionary Games and Population Dynamics , 1998 .

[60]  Axel W. Krings,et al.  Dynamic hybrid fault models and the applications to wireless sensor networks (WSNs) , 2008, MSWiM '08.

[61]  Melania Pintilie,et al.  Competing Risks: A Practical Perspective , 2006 .

[62]  Seif Haridi,et al.  Distributed Algorithms , 1992, Lecture Notes in Computer Science.

[63]  Zhanshan Ma,et al.  Competing Risks Analysis of Reliability, Survivability, and Prognostics and Health Management (PHM) , 2008, 2008 IEEE Aerospace Conference.