Hierarchical Test Sequencing for Complex Systems

Boumen, R.; Ruan, Sui; Jong, Ivo de; Mortel-Fronczak, J.M. van de; Rooda, J.E.; Pattipati, Krishna R.

doi:10.1109/tsmca.2009.2014550

Cited by 17 publications

(5 citation statements)

References 9 publications

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“…In addition, efficient tests are difficult to develop for these complex systems [26]. Due to design complexity, knowledge about the system is typically "spread" over different engineers.…”

Section: Model-based Methodsmentioning

confidence: 99%

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Knowledge-Driven Board-Level Functional Fault Diagnosis

Zhang

Chakrabarty

et al. 2017

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The semiconductor industry continues to relentlessly advance silicon technology scaling into the deep-submicron (DSM) era. High integration levels and structured design methods enable complex systems that can be manufactured in high volume.However, due to increasing integration densities and high operating speeds, subtle manifestation of defects leads to functional failures at the board level. Functional fault diagnosis is, therefore, necessary for board-level product qualification. However, ambiguous diagnosis results can lead to long debug times and wrong repair actions, which significantly increase repair cost and adversely impact yield.

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“…In addition, efficient tests are difficult to develop for these complex systems [26]. Due to design complexity, knowledge about the system is typically "spread" over different engineers.…”

Section: Model-based Methodsmentioning

confidence: 99%

“…A number of board-level fault diagnosis techniques have been presented in the literature [9,12,15,[23][24][25][26][27][28][29].…”

Section: Overview Of State-of-the-artmentioning

confidence: 99%

Knowledge-Driven Board-Level Functional Fault Diagnosis

Zhang

Chakrabarty

et al. 2017

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“…Under the CM design, testability design and maintainability design are two essential projects to determine supportability, enhance reliability, and guarantee safety during lifetime device management [ 8 ]. Testability design analyses the system’s internal structures, selects test projects and arranges test procedures, estimates the system operation condition, and locates failure modes.…”

Section: Introductionmentioning

confidence: 99%

Test Strategy Optimization Based on Soft Sensing and Ensemble Belief Measurement

Mei

Liu

Tang³

et al. 2022

Sensors

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Resulting from the short production cycle and rapid design technology development, traditional prognostic and health management (PHM) approaches become impractical and fail to match the requirement of systems with structural and functional complexity. Among all PHM designs, testability design and maintainability design face critical difficulties. First, testability design requires much labor and knowledge preparation, and wastes the sensor recording information. Second, maintainability design suffers bad influences by improper testability design. We proposed a test strategy optimization based on soft-sensing and ensemble belief measurements to overcome these problems. Instead of serial PHM design, the proposed method constructs a closed loop between testability and maintenance to generate an adaptive fault diagnostic tree with soft-sensor nodes. The diagnostic tree generated ensures high efficiency and flexibility, taking advantage of extreme learning machine (ELM) and affinity propagation (AP). The experiment results show that our method receives the highest performance with state-of-art methods. Additionally, the proposed method enlarges the diagnostic flexibility and saves much human labor on testability design.

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“…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][22][23] and search algorithm based on combination optimization [24][25][26][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.…”

Section: Introductionmentioning

confidence: 99%

Testability Modeling and Test Point Optimization Method of Multi-State System

Wang

2020

IEEE Access

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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.

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Hierarchical Test Sequencing for Complex Systems

Cited by 17 publications

References 9 publications

Knowledge-Driven Board-Level Functional Fault Diagnosis

Knowledge-Driven Board-Level Functional Fault Diagnosis

Test Strategy Optimization Based on Soft Sensing and Ensemble Belief Measurement

Testability Modeling and Test Point Optimization Method of Multi-State System

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