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- W4376608525 abstract "Industry 4.0 (I4.0) solutions are realizing the goal of intelligent factories using digital transformation of the manufacturing and production industries. The Machine Vision System (MVS) ensures the required quality measures of vision-based inspection tasks in smart factories. The recent studies fused various trending technologies such as Industrial IoT (IIoT), Edge Computing, Artificial Intelligence, etc., to improve the performance of MVS in terms of reduced latency, improved inspection accuracy, etc. However, these studies did not focus on the reliability aspect and its impact on system performance when component(s) of MVS becomes faulty. Hence, this work proposes a Bayesian network-based framework for identifying defective component(s) in MVS. Analysis reveals that the proposed solution is suitable for reliability analysis and faulty component(s) identification of MVS to ensure the quality control in vision-based industrial applications." @default.
- W4376608525 created "2023-05-17" @default.
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- W4376608525 date "2022-12-21" @default.
- W4376608525 modified "2023-09-23" @default.
- W4376608525 title "Bayesian Network based Reliability Analysis in Edge Computing enabled Machine Vision System" @default.
- W4376608525 doi "https://doi.org/10.1109/iatmsi56455.2022.10119369" @default.
- W4376608525 hasPublicationYear "2022" @default.
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