Distributed Process Monitoring for Multiagent Systems Through Cognitive Learning

Chen, Hongtian; Dogru, Oguzhan; Varanasi, Santhosh Kumar; Yin, Xunyuan; Huang, Biao

doi:10.1109/tcds.2022.3214424

Cited by 5 publications

(1 citation statement)

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“…With the growth of industrial process size, the centralized design becomes increasingly demanding in terms of memory and computing power, resulting in poor flexibility and high cost. Therefore, there is a strong research increase in distributed data-driven FD [26]. For example, ref.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data-Driven Fault Detection Method Based on Stable Kernel Representation for Dynamic Systems

Wang

Peng

Xie

et al. 2023

Sensors

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With the steady improvement of advanced manufacturing processes and big data technologies, modern industrial systems have become large-scale. To enhance the sensitivity of fault detection (FD) and overcome the drawbacks of the centralized FD framework in dynamic systems, a new data-driven FD method based on Hellinger distance and subspace techniques is proposed for dynamic systems. Specifically, the proposed approach uses only system input/output data collected via sensor networks, and the distributed residual signals can be generated directly through the stable kernel representation of the process. Based on this, each sensor node can obtain the identical residual signal and test statistic through the average consensus algorithms. In addition, this paper integrates the Hellinger distance into the residual signal analysis for improving the FD performance. Finally, the effectiveness and accuracy of the proposed method have been verified in a real multiphase flow facility.

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Section: Introductionmentioning

confidence: 99%