Abstract
Sensor data-based test selection optimization is the basis for designing a test work, which ensures that the system is tested under the constraint of the conventional indexes such as fault detection rate (FDR) and fault isolation rate (FIR). From the perspective of equipment maintenance support, the ambiguity isolation has a significant effect on the result of test selection. In this paper, an improved test selection optimization model is proposed by considering the ambiguity degree of fault isolation. In the new model, the fault test dependency matrix is adopted to model the correlation between the system fault and the test group. The objective function of the proposed model is minimizing the test cost with the constraint of FDR and FIR. The improved chaotic discrete particle swarm optimization (PSO) algorithm is adopted to solve the improved test selection optimization model. The new test selection optimization model is more consistent with real complicated engineering systems. The experimental result verifies the effectiveness of the proposed method.
Highlights
High technologies contribute a lot to the improvement of the performance of complex equipment
Test optimization selection is a process of selecting the proper test set from all the available test set under the constraints including minimum test cost, test cycle, fault detection rate (FDR) index, fault isolation rate (FIR) index, test resources, and engineering-oriented constraint rules [5]
Both improved chaotic discrete PSO (ICDPSO) and genetic algorithm (GA) have the same minimum test cost of 7 and the success rates of searching out optimal solution are both 100%, the average running time of GA is more than 2 times that of ICDPSO
Summary
High technologies contribute a lot to the improvement of the performance of complex equipment. Computational intelligence (CI) theories such as evolutionary algorithms [22, 23], artificial neural networks [24], cognitive map analysis [25], Physarum solver [26,27,28], fuzzy sets [29,30,31], belief function [32,33,34], PSO [35,36,37], and so on [38], have been widely used to cope the complex problems including the permutation flow shop problem [39], supply chain network [40, 41], traveling salesman problem [42], pattern recognition [43,44,45,46], power system [47], product design and manufacturing [48], and so on [49,50,51,52] Based on this progress in CI, many nature inspired approaches have been proposed to solve test selection optimization problem, such as the greedy strategy [53], the genetic algorithm [54, 55], the evolutionary algorithm [56, 57], and so on [58].
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