A Generalized Fault Coverage Model for Linear Time-Invariant Systems

This paper proposes a fault coverage model for linear time-invariant (LTI) systems subject to uncertain input. A state-space representation, defined by the state-transition matrix, and the input matrix, is used to represent LTI system dynamic behavior. The uncertain input is considered to be unknown but bounded, where the bound is defined by an ellipsoid. The state-transition matrix, and the input matrix must be such that, for any possible input, the system dynamics meets its intended function, which can be defined by some performance requirements. These performance requirements constrain the system trajectories to some region of the state-space defined by a symmetrical polytope. When a fault occurs, the state-transition matrix, and the input matrix might be altered; and then, it is guaranteed the system survives the fault if all possible post-fault trajectories are fully contained in the region of the state-space defined by the performance requirements. This notion of guaranteed survivability is the basis to model (in the context of LTI systems) the concept of fault coverage, which is a probabilistic measure of the ability of the system to keep delivering its intended function after a fault. Analytical techniques to obtain estimates of the proposed fault coverage model are presented. To illustrate the application of the proposed model, two examples are discussed.

[1]  Kishor S. Trivedi,et al.  Decomposition in Reliability Analysis of Fault-Tolerant Systems , 1983, IEEE Transactions on Reliability.

[2]  Pravin Varaiya,et al.  On Ellipsoidal Techniques for Reachability Analysis. Part I: External Approximations , 2002, Optim. Methods Softw..

[3]  J. Devooght,et al.  Probabilistic Reactor Dynamics —I: The Theory of Continuous Event Trees , 1992 .

[4]  Maurice G. Kendall,et al.  A Course in the Geometry of n Dimensions , 1962 .

[5]  Franco Blanchini,et al.  Set invariance in control , 1999, Autom..

[6]  A. Kurzhanski,et al.  Ellipsoidal Calculus for Estimation and Control , 1996 .

[7]  Dhiraj K. Pradhan,et al.  Fault-tolerant computer system design , 1996 .

[8]  Thomas F. Arnold,et al.  The Concept of Coverage and Its Effect on the Reliability Model of a Repairable System , 1973, IEEE Transactions on Computers.

[9]  R. A. Doney,et al.  4. Probability and Random Processes , 1993 .

[10]  Tunc Aldemir,et al.  Computer-Assisted Markov Failure Modeling of Process Control Systems , 1987, IEEE Transactions on Reliability.

[11]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[12]  E. Yaz Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[13]  Fred C. Schweppe,et al.  Uncertain dynamic systems , 1973 .

[14]  Jean Arlat,et al.  Coverage Estimation Methods for Stratified Fault Injection , 1999, IEEE Trans. Computers.

[15]  C. Constantinescu Estimation of coverage probabilities for dependability validation of fault-tolerant computing systems , 1994, Proceedings of COMPASS'94 - 1994 IEEE 9th Annual Conference on Computer Assurance.

[16]  Hermann Kopetz,et al.  Dependability: Basic Concepts and Terminology , 1992 .

[17]  Felix L. Chernousko,et al.  Properties of the Optimal Ellipsoids Approximating the Reachable Sets of Uncertain Systems , 2004 .

[18]  Jean Arlat,et al.  Estimators for fault tolerance coverage evaluation , 1993, FTCS-23 The Twenty-Third International Symposium on Fault-Tolerant Computing.

[19]  Niraj K. Jha,et al.  Fault-tolerant computer system design , 1996, IEEE Parallel & Distributed Technology: Systems & Applications.

[20]  D. Bertsimas,et al.  Moment Problems and Semidefinite Optimization , 2000 .

[21]  F. L. Chernousko What is Ellipsoidal Modelling and How to Use It for Control and State Estimation , 1999 .

[22]  Barry W. Johnson,et al.  Coverage Estimation Using Statistics of the Extremes for When Testing Reveals No Failures , 2002, IEEE Trans. Computers.

[23]  W. C. Carter,et al.  Reliability modeling techniques for self-repairing computer systems , 1969, ACM '69.

[24]  Carol-Sophie Smidts,et al.  Probabilistic dynamics as a tool for dynamic PSA , 1996 .

[25]  K. Mistry,et al.  Telecommunications power architectures: distributed or centralized , 1989, Conference Proceedings., Eleventh International Telecommunications Energy Conference.

[26]  Pravin Varaiya,et al.  Ellipsoidal Techniques for Reachability Analysis , 2000, HSCC.

[27]  Jean Arlat,et al.  Fault Injection and Dependability Evaluation of Fault-Tolerant Systems , 1993, IEEE Trans. Computers.

[28]  A. Amendola,et al.  Event Sequences and Consequence Spectrum: A Methodology for Probabilistic Transient Analysis , 1981 .

[29]  Pierre-Etienne Labeau,et al.  Dynamic reliability: towards an integrated platform for probabilistic risk assessment , 2000, Reliab. Eng. Syst. Saf..

[30]  Kishor S. Trivedi,et al.  Coverage Modeling for Dependability Analysis of Fault-Tolerant Systems , 1989, IEEE Trans. Computers.

[31]  Stephen P. Boyd,et al.  Generalized Chebyshev Bounds via Semidefinite Programming , 2007, SIAM Rev..

[32]  P. S. Babcock,et al.  An automated environment for optimizing fault-tolerant systems designs , 1991, Annual Reliability and Maintainability Symposium. 1991 Proceedings.

[33]  Marvin Rausand,et al.  System Reliability Theory , 2020, Wiley Series in Probability and Statistics.

[34]  and Charles K. Taft Reswick,et al.  Introduction to Dynamic Systems , 1967 .

[35]  Ioana Popescu,et al.  Optimal Inequalities in Probability Theory: A Convex Optimization Approach , 2005, SIAM J. Optim..

[36]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[37]  C. Constantinescu Using multi-stage and stratified sampling for inferring fault-coverage probabilities , 1995 .

[38]  G. Grimmett,et al.  Probability and random processes , 2002 .

[39]  Stephen P. Boyd,et al.  Graph Implementations for Nonsmooth Convex Programs , 2008, Recent Advances in Learning and Control.