Based on self-learning dictionary circuit board fault diagnosis device

Tang, Yongwei; Wang, Chunmei; Wang, Maoli; Hao, Huijuan; Zhao, Jingbo

doi:10.1109/iaeac.2017.8054506

Cited by 13 publications

(1 citation statement)

References 7 publications

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“…A self-learning dictionary and fault tree analysis-based fault diagnosis method was designed for detecting the faults of outdoor and energy-limited IoT nodes and performed in realworld test-beds [39]. More specifically, a fault is detected if the offset exceeds the rating of ±20%.…”

Section: B Fault Detection Methodsmentioning

confidence: 99%

SILOS: An Intelligent Fault Detection Scheme for Solar Insecticidal Lamp IoT With Improved Energy Efficiency

Yang

Liu

et al. 2023

IEEE Internet Things J.

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Solar Insecticidal Lamp Internet of Things (SIL-IoTs) nodes are susceptible to failures due to the harsh environment, burn-in, theft, and vandalism in the agricultural setting. Most state-of-the-art research mainly focuses on fault detection without considering hardware and communication performance in terms of potential fault modes, energy consumption, and network loads. This study presents a completely decentralized solution, namely a SIL-Oriented binary Sliding window-based fault self-detection scheme (SILOS), which can be performed on each SIL-IoTs node. The problem we are trying to answer is how to detect faults as accurately as possible while keeping the communication overhead, memory, and computational costs low. Specifically, we develop a fault dictionary concept to 1) model the faults of SIL-IoTs nodes, 2) construct the fault dictionary according to the characteristics of measurements, and 3) detect faults via the fault dictionary. In addition, a binarybased sliding window (BSW) fault self-detection approach is proposed to save detection costs and reduce the false alarm rate (with only 92B system caches). A series of experiments are performed to evaluate the performance of the proposed method. The result demonstrates that the BSW method can detect faults with an average accuracy of 99.14% with less than 1% energy consumption. By only sending the fault code, 71B data (i.e., data transmitting and forwarding) can be reduced, saving energy consumption, and decreasing network congestion.

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Section: B Fault Detection Methodsmentioning

confidence: 99%