Fast-bunching design of compact heavy ion RFQ linac

Wang, Kedong; Yuan, Y. J.; Yin, Xin; Jiang, Yang; Du, Haiying; Li, Zhongshan; Li, Xiaoni; Zhao, He; Dong, Zhihui; Liu, Jie; Xia, J.W.

doi:10.1007/s41365-018-0514-x

Cited by 3 publications

(1 citation statement)

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“…A fault is an unacceptable deviation that exceeds the design specifications of a measurement parameter, which reflects the performance or state of the equipment. The traditional fault detection [7] and diagnosis [8][9][10] processes are to check for faults and give the fault category after the fault occurs, which are a kind of lagging processing method. However, before a fault occurs, equipment parameters or status may be abnormal, which means that they may be different from those in the normal state.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven modelling for online fault pre‐warning in thermal power plant using incremental Gaussian mixture regression

Jin,

Si,

Dong

et al. 2023

Can J Chem Eng

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This study introduces a data‐driven model for online fault pre‐warning in thermal power plants using incremental Gaussian mixture regression. To tackle the issue of outdated parameters in existing fault pre‐warning models, this study puts forth an incremental Gaussian mixture regression that leverages the merging of Gaussian components to reconstruct the model and enable online modelling. Due to its criticality, a forgetting factor is introduced during the merging process to efficiently manage the weight allocation between present and historical patterns, thereby guaranteeing the model's accuracy. The results of the sine function case demonstrate that the incremental Gaussian mixture regression (IGMR) model exhibits excellent pattern control performance and modelling efficiency. Furthermore, the IGMR model is employed to forecast parameter alterations in pulverizer blockages with mode switching, and experimental validation indicates that IGMR precisely anticipates parameter changes following mode switching. Compared to on‐site solutions, the pre‐warning of coal blockage has a clear advantage in advance warning.

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