Evidence updating with static and dynamical performance analyses for industrial alarm system design

Xiaobin Xu, Xu Weng, Dongling Xu, Haiyang Xu, Yanzhu Hu, Jianning Li

Research output: Contribution to journalArticlepeer-review


In the Dempster–Shafer theory (DST) of evidence, the alarm evidence updating-based method can effectively deal with the uncertainty of the monitored process variable so as to significantly reduce the false alarm rates (FAR) and missed alarm rates (MAR) of the industrial alarm system. But the price of the decrease of FAR and MAR is the increase of the averaged alarm delay (AAD). In order to obtain better comprehensive performance, besides the accuracy indices (FAR and MAR), the sensitivity index (AAD) should be considered simultaneously in the alarm system parameter optimization design. In the framework of DST, firstly, this paper defines the static and dynamical performance indices in the alarm evidence space which are compatible with FAR/MAR/AAD in the process variable space. But the former can measure the performance of the DST-based alarm systems more naturally and elaborately than the latter; secondly, a systematic parameter optimization design procedure for the alarm system is investigated by using these new indices and the tradeoff among them. Finally, two typical numerical experiments and an industrial case are provided to illustrate the effectiveness of the static and dynamical indices for improving the comprehensive performance of the DST-based alarm systems.

Original languageEnglish
JournalISA Transactions
Early online date9 Sept 2019
Publication statusE-pub ahead of print - 9 Sept 2019


  • Alarm system design
  • Data-driven design
  • Dempster–Shafer theory of evidence
  • Epistemic and aleatory uncertainties
  • Information fusion


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