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Data mining algorithm for correlation analysis of industrial alarms

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Abstract

Alarm is important in industrial safety management. The technique to capture the correlation information of alarm variables, especially from historical alarm data, is very beneficial for risk prediction and prevention, but the task is very difficult because many alarm tags are associated with a single process variable and often alarm tags are tackled with any specific process variables. In this paper, a general weight-based multi-state sequential algorithm for correlation analysis is applied for alarm data to improve the validity and accuracy of alarm clustering combined with the traditional agglomerative hierarchical clustering algorithm. To make the direction between alarm variables, this paper proposes a vector correlation concept and use conditional probability to measure the alarm correlation among different tags comparable. The method breaks through the limitations of the traditional research on alarm variables correlation that distinguishes sequential alarms with non sequential alarms, or treats differently between regular and irregular alarms. Furthermore, a two-dimensional matrix is used to show the vector correlation of alarm variables intuitively and visually. The data mining algorithm is shown to be able to find out the vector correlation of alarm variables effectively and correctly when applied in the analysis of power plant alarm data.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (61573138).

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Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China

    Tingshun Li & Wen Tan

  2. China Power Information Technology Co., Ltd., Beijing, 100000, China

    Xiaojie Li

Authors
  1. Tingshun Li
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  2. Wen Tan
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  3. Xiaojie Li
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Correspondence to Tingshun Li.

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Li, T., Tan, W. & Li, X. Data mining algorithm for correlation analysis of industrial alarms. Cluster Comput 22 (Suppl 4), 10133–10143 (2019). https://doi.org/10.1007/s10586-017-1170-3

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  • DOI: https://doi.org/10.1007/s10586-017-1170-3

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