Optimal Test Points Selection Based on Multi-Objective Genetic Algorithm

Abstract

A new approach to select an optimal set of test points is proposed. The described method uses fault-wise table and multiobjective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The problem of optimal test points selection is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method efficiently and accurately finds the optimal set of test points and is practical for large scale systems.

Minimizing test-point allocation to improve diagnosability in business process models

“…The proposal by Spanache et al (2004) allocates sensors according to an economic criterion, and presents a method to obtain an optimal-cost sensor system for a certain degree of diagnosability. Zhang et al (2009b) allocates test points in circuits and physical systems using a genetic algorithm. Since the signal propagation in this kind of system flows in a different way to that of the dataflow of a business process, this method is inapplicable in the context discussed in this paper.…”

System level test selection based on combinatorial dependency matrix

“…Test selection is one of the most important tasks in testability design for equipment, whose goal is searching for the optimal test set to satisfy the requirements of fault diagnosis indexes. The universal way to make test selection is as follows [1]: to analyze the object's fault modes and preliminarily select tests at first, and then to construct a "0/1" type matrix which is called typical dependency matrix (TDM) to describe the relationship between equipment's faults and tests, and based on the matrix to establish a mathematical model to describe the test selection problem, and to search out the optimal test set through optimization algorithms in the end. Test point selection is a special case of test selection.…”

Research on the Arranging and Patchwork Design for Storage Tank Bottom Based on Sinovation

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