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This paper considers the sequential fault diagnosis problem with unreliable tests which exist widely in practice. This problem involves real-time inference of the most likely set of failure sources, i.e., fault state, based on unreliable test outcomes. The purpose of this paper is to optimize test set and diagnostic strategy so as to cut down the test cost while isolating the fault accurately, and a method for optimal diagnosis strategy based on partially observable Markov decision process (POMDP) is presented. The components of the POMDP tailed to optimizing the diagnostic strategy are specified, and the solution to the POMDP-based model, namely the optimal strategy, is obtained to describe the optimal test sequence for fault diagnosis. The performance of the proposed method is evaluated with simulation experiments. All the results indicate that this method performs good in diagnostic efficiency and accuracy, even compared with the strategies of traditional methods.

Sequential fault diagnosis is an approach that realizes fault isolation by executing the optimal test step by step. The strategy used, i.e., the sequential diagnostic strategy, has great influence on diagnostic accuracy and cost. Optimal sequential diagnostic strategy generation is an important step in the process of diagnosis system construction, which has been studied extensively in the literature. However, previous algorithms either are designed for single mode systems or do not consider test placement cost. They are not suitable to solve the sequential diagnostic strategy generation problem considering test placement cost for multimode systems. Therefore, this problem is studied in this paper. A formulation is presented. Two algorithms are proposed, one of which is realized by system transformation and the other is newly designed. Extensive simulations are carried out to test the effectiveness of the algorithms. A real-world system is also presented. All the results show that both of them have the ability to solve the diagnostic strategy generation problem, and they have different characteristics.

Abstract: Once all available measurements are determined, the highest testability index of a complex system is determined. To achieve such highest index with the lowest test cost, AND/OR graph search algorithms were developed for years to determine an optimal or near-optimal test sequence. However, in most cases, achieving the highest testability index induces extremely high test cost. The purpose of this paper is to optimize test set and test sequence so as to cut down the test cost while keeping the required, not necessarily the highest, FIR (Fault Isolation Rate) satisfied. Traditionally, this is an NP-Complete problem, which makes the computation of optimal test set impractical for even the moderate-sized model. In this paper, a greedy method is proposed to get the optimized test set. Then, we combine the greedy method with discrete binary particle swarm optimization (DPSO) to construct a test sequential tree. With the specified FIR requirement satisfied, the lowest test cost is achieved. The proposed algorithms are illustrated and tested in a range of real-world systems. The effectiveness and accurateness of the proposed algorithm is verified by computational results. [Copyright &y& Elsevier]

The problem of sequential fault diagnosis is to construct a diagnosis tree that can isolate the failure sources with minimal test cost. Pervious sequential fault diagnosis strategy generating algorithms only consider the execution cost at application stage, which may result in a solution with poor quality from the view of life cycle cost. Furthermore, due to the fact that uncertain information exists extensively in the real-world systems, the tests are always imperfect. In order to reduce the cost of fault diagnosis in the realistic systems, the sequential fault diagnosis problem with imperfect tests considering life cycle cost is presented and formulated in this work, which is an intractable NP-hard AND/OR decision tree construction problem. An algorithm based on AND/OR graph search is proposed to solve this problem. Heuristic search based on information theory is applied to generate the sub-tree in the algorithm. Some practical issues such as the method to improve the computational efficiency and the diagnosis strategy with multi-outcome tests are discussed. The algorithm is tested and compared with previous algorithms on the simulated systems with different scales and uncertainty. Application on a wheel momentum system of a spacecraft is studied in detail. Both the simulation and application results suggest that the cost of the diagnosis strategy can be reduced significantly by using the proposed algorithm, especially when the placement cost of the tests constitutes a large part of the total cost. [ABSTRACT FROM AUTHOR]

The problem of generating the sequence of tests required to reach a diagnostic conclusion with minimum average cost, which is also known as a test-sequencing problem, is considered. The traditional test-sequencing problem is generalized here to include asymmetrical tests. In general, the next test to execute depends on the results of previous tests. Hence, the test-sequencing problem can naturally be formulated as an optimal binary AND/OR decision tree construction problem, whose solution is known to be N P-hard. Our approach is based on integrating concepts from one-step look-ahead heuristic algorithms and basic ideas of Huffman coding to construct an AND/OR decision tree bottom-up as opposed to heuristics proposed in the literature that construct the AND/OR trees top-down. The performance of the algorithm is demonstrated on numerous test cases, with various properties. [ABSTRACT FROM AUTHOR]

Fault diagnosis is crucial for the reduction of test and integration time as well as downtime of complex systems. In this article, we present a model-based approach to derive tests and test sequences for sequential fault diagnosis. This approach offers advantages over methods that are based on test coverage of explicit fault states, represented in matrix form. Functional models are more easily adapted to design changes and constitute a complete information source for test selection on a given abstraction level. We introduce our approach and implementation with a theoretical example. We demonstrate its practical use in three case studies, and for these cases we obtain cost reductions of up to 59% compared to the matrix-based approach [ABSTRACT FROM PUBLISHER]

• Test sequencing problem is simplified as a combinatorial problem comprising a basic test set with unnecessary tests. • A growing algorithm that chooses the failure states first and then finds the suitable test set for the selected failure states is presented. • Application scope for the growing algorithm is demonstrated in detail by stochastic simulation experiments. Test sequencing for binary systems is an NP-complete problem. In this study, we introduce a novel algorithm for this problem, which is defined as a growing algorithm. This algorithm chooses the failure states and finds a suitable test set for the selected failure states. This can avoid the backtracking approach of the traditional algorithms. Three main procedures are presented to illustrate the growing algorithm: (1) a test sequencing problem is simplified as a combinatorial problem comprising a basic test set with unnecessary tests; (2) the optimal test sequence generating algorithm (OTSGA) is proposed for an individual failure state; and (3) the priority levels of the failure states are determined based on their prior probabilities. Finally, a circuit system is used to show how the growing algorithm works, and five real-word D matrices are employed to validate the universality and stability of the algorithm. Subsequently, the application scope for the growing algorithm is demonstrated in detail by stochastic simulation experiments. This growing algorithm is suitable for large-scale systems with a sparse D matrix, and it obtains good calculation results with a short running time and high efficiency. [ABSTRACT FROM AUTHOR]

Sequential fault diagnosis is an approach that realizes fault isolation by executing the optimal test step by step. The strategy used, i.e., the sequential diagnostic strategy, has great influence on diagnostic accuracy and cost. Optimal sequential diagnostic strategy generation is an important step in the process of diagnosis system construction, which has been studied extensively in the literature. However, previous algorithms either are designed for single mode systems or do not consider test placement cost. They are not suitable to solve the sequential diagnostic strategy generation problem considering test placement cost for multimode systems. Therefore, this problem is studied in this paper. A formulation is presented. Two algorithms are proposed, one of which is realized by system transformation and the other is newly designed. Extensive simulations are carried out to test the effectiveness of the algorithms. A real-world system is also presented. All the results show that both of them have the ability to solve the diagnostic strategy generation problem, and they have different characteristics.