Resilience-Driven System Design of Complex Engineered Systems

Most engineered systems are designed with a passive and fixed design capacity and, therefore, may become unreliable in the presence of adverse events. Currently, most engineered systems are designed with system redundancies to ensure required system reliability under adverse events. However, a high level of system redundancy increases a system’s life-cycle cost (LCC). Recently, proactive maintenance decisions have been enabled through the development of prognostics and health management (PHM) methods that detect, diagnose, and predict the effects of adverse events. Capitalizing on PHM technology at an early design stage can transform passively reliable (or vulnerable) systems into adaptively reliable (or resilient) systems while considerably reducing their LCC. In this paper, we propose a resilience-driven system design (RDSD) framework with the goal of designing complex engineered systems with resilience characteristics. This design framework is composed of three hierarchical tasks: (i) the resilience allocation problem (RAP) as a top-level design problem to define a resilience measure as a function of reliability and PHM efficiency in an engineering context, (ii) the system reliability-based design optimization (RBDO) as the first bottom-level design problem for the detailed design of components, and (iii) the system PHM design as the second bottom-level design problem for the detailed design of PHM units. The proposed RDSD framework is demonstrated using a simplified aircraft control actuator design problem resulting in a highly resilient actuator with optimized reliability, PHM efficiency and redundancy for the given parameter settings. [DOI: 10.1115/1.4004981]

[1]  Way Kuo,et al.  Determining Component Reliability and Redundancy for Optimum System Reliability , 1977, IEEE Transactions on Reliability.

[2]  A. H. Christer,et al.  Delay Time Models of Industrial Inspection Maintenance Problems , 1984 .

[3]  A. Dhingra Optimal apportionment of reliability and redundancy in series systems under multiple objectives , 1992 .

[4]  Robert E. Uhrig,et al.  Monitoring and diagnosis of rolling element bearings using artificial neural networks , 1993, IEEE Trans. Ind. Electron..

[5]  K. F. Martin,et al.  A review by discussion of condition monitoring and fault diagnosis in machine tools , 1994 .

[6]  Terje Aven Condition based replacement policiesa counting process approach , 1996 .

[7]  C. Bengtsson,et al.  Status and trends in transformer monitoring , 1996 .

[8]  Dan M. Frangopol,et al.  Life-cycle cost design of deteriorating structures , 1997 .

[9]  Clifford R. Burrows,et al.  Fault diagnosis of a hydraulic actuator circuit using neural networks—an output vector space classification approach , 1997 .

[10]  Stefan Frischemeier,et al.  Electrohydrostatic actuators for aircraft primary flight control - types, modelling and evaluation , 1997 .

[11]  James R. McDonald,et al.  The use of artificial neural networks for condition monitoring of electrical power transformers , 1998, Neurocomputing.

[12]  Garry D. Peterson,et al.  Complex Adaptive Systems: Use and Analysis of Complex Adaptive Systems in Ecosystem Science: Overview of Special Section , 1998, Ecosystems.

[13]  Y. Hsieh,et al.  Genetic algorithms for reliability design problems , 1998 .

[14]  S. Luthar Poverty and Children′s Adjustment , 1999 .

[15]  Stephen Osder,et al.  Practical View of Redundancy Management Application and Theory , 1999 .

[16]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[17]  E. M. Just,et al.  Computer Model for Prediction of PCB Dechlorination and Biodegradation Endpoints , 1999 .

[18]  C. Adjiman,et al.  Global optimization of mixed‐integer nonlinear problems , 2000 .

[19]  S. Luthar,et al.  The construct of resilience: a critical evaluation and guidelines for future work. , 2000, Child development.

[20]  D. E. Dimla,et al.  Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods , 2000 .

[21]  Michael E. Tipping Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[22]  Antoine Grall,et al.  A condition-based maintenance policy for stochastically deteriorating systems , 2002, Reliab. Eng. Syst. Saf..

[23]  M. Zuo,et al.  Optimal Reliability Modeling: Principles and Applications , 2002 .

[24]  Xiaoping Du,et al.  Sequential Optimization and Reliability Assessment Method for Efficient Probabilistic Design , 2004, DAC 2002.

[25]  Takeshi Yanagisawa,et al.  Degradation of InGaN blue light-emitting diodes under continuous and low-speed pulse operations , 2003, Microelectron. Reliab..

[26]  Marvin Rausand,et al.  System Reliability Theory: Models, Statistical Methods, and Applications , 2003 .

[27]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[28]  Alice E. Smith,et al.  An ant colony optimization algorithm for the redundancy allocation problem (RAP) , 2004, IEEE Transactions on Reliability.

[29]  F. Muzi,et al.  Vibro-acoustic techniques to diagnose power transformers , 2004, IEEE Transactions on Power Delivery.

[30]  Ratna Babu Chinnam,et al.  A neuro-fuzzy approach for estimating mean residual life in condition-based maintenance systems , 2004 .

[31]  K. K. Choi,et al.  Enriched Performance Measure Approach (PMA+) for Reliability-Based Design Optimization , 2004 .

[32]  Steven Kmenta,et al.  Scenario-Based Failure Modes and Effects Analysis Using Expected Cost , 2004 .

[33]  S. Rahman,et al.  A univariate dimension-reduction method for multi-dimensional integration in stochastic mechanics , 2004 .

[34]  N. G. Shrive,et al.  Intelligent structural health monitoring: a civil engineering perspective , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[35]  T. P. Ryan,et al.  System Reliability Theory: Models, Statistical Methods, and Applications, Second Edition , 2005 .

[36]  Byeng D. Youn,et al.  Performance Moment Integration (PMI) Method for Quality Assessment in Reliability-Based Robust Design Optimization , 2005 .

[37]  Mark Schwabacher,et al.  A Survey of Data -Driven Prognostics , 2005 .

[38]  S. Rahman,et al.  Decomposition methods for structural reliability analysis , 2005 .

[39]  B. Youn,et al.  Enriched Performance Measure Approach for Reliability-Based Design Optimization. , 2005 .

[40]  N.S. Clements,et al.  PHM as a Design Variable in Air Vehicle Conceptual Design , 2005, 2005 IEEE Aerospace Conference.

[41]  G. Bonanno,et al.  Resilience to loss in bereaved spouses, bereaved parents, and bereaved gay men. , 2005, Journal of personality and social psychology.

[42]  Hsien-Yu Tseng,et al.  A neural network application for reliability modelling and condition-based predictive maintenance , 2005 .

[43]  Viliam Makis,et al.  Adaptive state detection of gearboxes under varying load conditions based on parametric modelling , 2006 .

[44]  Stephan Ebersbach,et al.  The investigation of the condition and faults of a spur gearbox using vibration and wear debris analysis techniques , 2006 .

[45]  David Woods,et al.  Resilience Engineering: Concepts and Precepts , 2006 .

[46]  Erik Hollnagel Achieving System Safety by Resilience Engineering , 2006 .

[47]  N. Sepehri,et al.  Hydraulic actuator leakage quantification scheme using extended Kalman filter and sequential test method , 2006, 2006 American Control Conference.

[48]  Zissimos P. Mourelatos,et al.  A Single-Loop Approach for System Reliability-Based Design Optimization , 2006, DAC 2006.

[49]  K. Goebel,et al.  Fusing competing prediction algorithms for prognostics , 2006, 2006 IEEE Aerospace Conference.

[50]  C. Webb What Is the Role of Ecology in Understanding Ecosystem Resilience? , 2007 .

[51]  Lee J. Wells,et al.  Bayesian Reliability Based Design Optimization Using Eigenvector Dimension Reduction (EDR) Method , 2007, DAC 2007.

[52]  Lubica Benusková,et al.  Organization of the state space of a simple recurrent network before and after training on recursive linguistic structures , 2007, Neural Networks.

[53]  L. Bertling,et al.  Maintenance Management of Wind Power Systems Using Condition Monitoring Systems—Life Cycle Cost Analysis for Two Case Studies , 2007, IEEE Transactions on Energy Conversion.

[54]  ANDREW J. KERKHOFF,et al.  The Implications of Scaling Approaches for Understanding Resilience and Reorganization in Ecosystems , 2007 .

[55]  G. Bonanno,et al.  What predicts psychological resilience after disaster? The role of demographics, resources, and life stress. , 2007, Journal of consulting and clinical psychology.

[56]  Abhinav Saxena,et al.  Damage propagation modeling for aircraft engine run-to-failure simulation , 2008, 2008 International Conference on Prognostics and Health Management.

[57]  K. Häusler,et al.  Degradation model analysis of laser diodes , 2008 .

[58]  F.O. Heimes,et al.  Recurrent neural networks for remaining useful life estimation , 2008, 2008 International Conference on Prognostics and Health Management.

[59]  J.W. Hines,et al.  Prognostic algorithm categorization with PHM Challenge application , 2008, 2008 International Conference on Prognostics and Health Management.

[60]  Krishna R. Pattipati,et al.  Model-Based Prognostic Techniques Applied to a Suspension System , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[61]  Mark P. McDonald,et al.  Reliability-Based Optimization With Discrete and Continuous Decision and Random Variables , 2008 .

[62]  B. Youn,et al.  Bayesian reliability-based design optimization using eigenvector dimension reduction (EDR) method , 2008 .

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

[64]  B. Youn,et al.  Eigenvector dimension reduction (EDR) method for sensitivity-free probability analysis , 2008 .

[65]  Kyung K. Choi,et al.  Reliability-Based Design Optimization Using Response Surface Method With Prediction Interval Estimation , 2008 .

[66]  Jianbo Yu,et al.  A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems , 2008, 2008 International Conference on Prognostics and Health Management.

[67]  Jing Pan,et al.  Prognostic Degradation Models for Computing and Updating Residual Life Distributions in a Time-Varying Environment , 2008, IEEE Transactions on Reliability.

[68]  Byeng D. Youn,et al.  A Generic Bayesian Framework for Real-Time Prognostics and Health Management (PHM) , 2009 .

[69]  Christophe Bérenguer,et al.  Predictive maintenance policy for a gradually deteriorating system subject to stress , 2009, Reliab. Eng. Syst. Saf..

[70]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[71]  Leandro dos Santos Coelho,et al.  An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications , 2009, Reliab. Eng. Syst. Saf..

[72]  Byeng D. Youn,et al.  Bayesian Reliability Analysis With Evolving, Insufficient, and Subjective Data Sets , 2009 .

[73]  Alaa Elwany,et al.  Residual Life Predictions in the Absence of Prior Degradation Knowledge , 2009, IEEE Transactions on Reliability.

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

[75]  Glaucio H. Paulino,et al.  Single-Loop System Reliability-Based Design Optimization Using Matrix-Based System Reliability Method: Theory and Applications , 2010 .

[76]  Enrico Zio,et al.  A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system , 2010, Reliab. Eng. Syst. Saf..

[77]  Byeng D. Youn,et al.  A Generic Sensor Network Design Framework Based on a Detectability Measure , 2010 .

[78]  Byeng D. Youn,et al.  Ensemble of Data-Driven Prognostic Algorithms with Weight Optimization and K-Fold Cross Validation , 2010 .

[79]  Ying Xiong,et al.  A new sparse grid based method for uncertainty propagation , 2010 .

[80]  Byeng D. Youn,et al.  Adaptive-sparse polynomial chaos expansion for reliability analysis and design of complex engineering systems , 2011 .