Abstract
Existing testability models are difficult to describe the multi-state characteristics of the system, so it is necessary to study the testability modeling method applicable to multi-state systems. A testability model with structure and function as the object is established in this paper. In order to describe the relationship between system state and test, the calculation method of the detectable state set of the test set is introduced. In order to quantitatively describe the testability of the system state, the concept of state detection rate is proposed for the first time. A test point optimization method that comprehensively considers the system fault detection rate, fault isolation rate, and state detection rate under the constraints of test cost is proposed. A numerical example shows that the best test set obtained by this method cannot only complete the system fault detection and isolation, but also obtain more system state.
Highlights
Compared with a binary system, a multi-state system considers one or more degraded states between the system and components from normal to fault
It is necessary to use a heuristic search algorithm to find test combinations that meet the requirements. Because it is suitable for test point selection coding, we choose genetic algorithm (GA) to search the optimal test set
It is shown that the optimal test set obtained by our method can detect more system states while ensuring fault detection rate (FDR) and fault isolation rate (FIR) indexes
Summary
Compared with a binary system, a multi-state system considers one or more degraded states between the system and components from normal to fault. The multi-signal flow model connects fault and test through the flow direction of functional information, describing the relationship between fault and test. In terms of test optimization of the binary system, scholars have made extensive research and put forward many optimization methods, mainly including sorting methods based on information theory [21-23] and search algorithm based on combination optimization [24-27] The former mainly uses information entropy to define the test importance of fault detection and fault isolation and takes it as the weight of test, preferentially selects the test with high weight until it meets the requirements of testability index.
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