Xisen Wen

Optimal Selection of Imperfect Tests for Fault Detection and Isolation

In this paper, we propose new formulations for the problem of test selection in the presence of imperfect tests in order to minimize the total costs of tests subject to lower bound constraints on fault detection and fault isolation. Our formulation allows tests to have multiple outcomes and delays caused by fault propagation, reporting, and transmission. Since the test selection problem is NP-hard even in the presence of perfect binary tests with no delays, we propose genetic algorithm (GA) and Lagrangian relaxation algorithm (LRA) to solve this problem. GA is a general approach for solving the problem with imperfect tests, including the scenarios with delayed and multiple test outcomes. The LRA is suitable for problems with perfect tests, including multiple outcomes. A key advantage of the LRA approach is that it provides an approximate duality gap, which is an upper bound measure of suboptimality of the solution. Our formulations and algorithms are tested on various real-world and simulated systems, and comparisons are made with previous test selection methods developed for perfect tests with no delays. The results show that our methods can efficiently solve the imperfect test selection problem. In addition, they have better performance (measured in terms of the number of tests used) than the methods in the literature for the perfect test selection cases. Finally, the GA has better computational efficiency than the LRA for all of the scenarios with perfect tests.

Dynamic Coupled Fault Diagnosis With Propagation and Observation Delays

In this paper, we propose a delay dynamic coupled fault diagnosis (DDCFD) model to deal with the problem of coupled fault diagnosis with fault propagation/transmission delays and observation delays with imperfect test outcomes. The problem is to determine the most likely set of faults and their time evolution that best explains the observed test outcomes over time. It is formulated as a combinatorial optimization problem, which is known to be NP-hard. Since the faults are coupled, the problem does not have a decomposable structure as, for example, in dynamic multiple fault diagnosis, where the coupled faults and delays are not taken into account. Consequently, we propose a partial-sampling method based on annealed maximum a posteriori (MAP) algorithm, a method that combines Markov chain Monte Carlo and simulated annealing, to deal with the coupled-state problem. By reducing the number of samples and by avoiding redundant computations, the computation time of our method is substantially smaller than the regular annealed MAP method with no noticeable impact on diagnostic accuracy. Besides the partial-sampling method, we also propose an algorithm based on block coordinate ascent and the Viterbi algorithm (BCV) to solve the DDCFD problem. It can be considered as an extension of the method used to solve the dynamic coupled fault diagnosis (DCFD) problem. The model and algorithms presented in this paper are tested on a number of simulated systems. The results show that the BCV algorithm has better accuracy but results in large computation time. It is only feasible for problems with small delays. The partial-sampling algorithm has a smaller computation time with an acceptable diagnostic accuracy. It can be used on systems with large delays and complex topological structure.

Maintainability-based facility layout optimum design of ship cabin

Maintainability of a mechanical system is one of the system design parameters that has a great impact in terms of ease of maintenance. In this paper, a methodology of facility layout optimum design for maintainability of a ship cabin is presented as a way to improve the efficiency and quality of maintainability design. The maintenance operating space, amount of hoisting, balance of cabin, distance requirement and personnel movement distance are all taken into account, and treated as objective functions. The mechanical functional constraints and some important layout experience are also considered and formulated as constraints. Thus, the mathematical model for maintainability layout combinatorial optimisation is constructed. According to the characteristics of maintainability-based facility layout problem, the particle swarm optimisation algorithm developed by Eberhart and Kennedy is modified to enhance the computational efficiency and solution accuracy. A hybrid position updating method is used to solve the optimisation problem with both continuous and discrete variables. The dynamic neighbourhood structure, dynamic inertia weight and adaptive mutation mode are modified to effectively solve the optimisation problem with multiple peak values. Finally, the methodology proposed is illustrated by simulation case and engineering application, and the results suggest that the methodology is effective.

Fuzzy grey relational analysis of design factors influencing on maintainability indices

Maintainability is a design attribute of a product, and its relationship with the design factors is complicated. In consideration of the fuzziness and uncertainty of design factors that influences the products maintainability indices, we propose a method based on fuzzy grey relational analysis to determine their prioritization in this paper. First, the evaluation methods of various design factors and how these factors affect product maintainability indices are analyzed. Then, the fuzzy grey relational analysis model of the relationship between the design factors and maintainability indices is set up, and the prioritization of design factors to maintainability indices is achieved. Finally, the proposed methods are applied on a subsystem of a manned spacecraft, which indicates that the proposed method can quantitatively analyze the influencing degree of the design factors to maintainability indices. This information can be used to guide maintainability indices evaluation modeling based on digital mock-up in the design phase and help to catch the major influencing design factors in maintainability design.