Integer and combinatorial optimization
Integer and combinatorial optimization
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Optimal and near-optimal algorithms for multiple fault diagnosiswith unreliable tests
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A hidden Markov model-based algorithm for fault diagnosis withpartial and imperfect tests
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On a multimode test sequencing problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Max-Product Algorithms for the Generalized Multiple-Fault Diagnosis Problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Test sequencing algorithms with unreliable tests
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Rollout strategies for sequential fault diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Novel Approach for Optimal Cost-Effective Design of Complex Repairable Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Lagrangian Relaxation Algorithm for Finding the MAP Configuration in QMR-DT
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Wavelet Band-Limiting Filter Approach for Fault Detection in Dynamic Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Sensor Placement for Fault Diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A factorial hidden Markov model (FHMM)-based reasoner for diagnosing multiple intermittent faults
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Probabilistic model-based diagnosis: an electrical power system case study
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
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In this paper, we consider a model for the dynamic multiple-fault diagnosis (DMFD) problem arising in online monitoring of complex systems and present a solution. This problem involves real-time inference of the most likely set of faults and their time-evolution based on blocks of unreliable test outcomes over time. In the DMFD problem, there is a finite set of mutually independent fault states, and a finite set of sensors (tests) is used to monitor their status. We model the dependence of test outcomes on the fault states via the traditional D-matrix (fault dictionary). The tests are imperfect in the sense that they can have missed detections, false alarms, or may be available asynchronously. Based on the imperfect observations over time, the problem is to identify the most likely evolution of fault states over time. The DMFD problem is an intractable NP-hard combinatorial optimization problem. Consequently, we decompose the DMFD problem into a series of decoupled subproblems, one for each sample epoch. For a single-epoch MFD, we develop a fast and high-quality deterministic simulated annealing method. Based on the sequential inferences, a local search-and-update scheme is applied to further improve the solution. Finally, we discuss how the method can be extended to dependent faults.