Silicon Carbide Bipolar Analog Circuits for Extreme Temperature Signal Conditioning

Roy, Sajib; Rashid, Arman Ur; Abbasi, Affan; Murphree, Robert; Hossain, Maksudul; Faruque, Asif; Metreveli, Alex; Zetterling, Carl-Mikael; Fraley, John; Sparkman, Brett; Mantooth, H. Alan

doi:10.1109/ted.2019.2928484

Cited by 9 publications

(1 citation statement)

References 16 publications

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“…Hence, all these methods assume that dependent single-signal operations and sequential procedures can make all diagnostic decisions. However, modern devices contain highly complicated logistic relationships [ 18 ] between test procedures and potential failure modes under many scenarios. Consequently, the testability design consumes too much human power and affects the testability design efficiency to prepare prior knowledge, especially under short production cycles.…”

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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Section: Introductionmentioning

confidence: 99%