Performance evaluation of variable or dynamic threshold alarm systems

Koorosh Aslansefat, Mahdi Bahar Gogani, Sohag Kabir, Mahdi Aliyari Shoorehdeli, Mostafa Yari

Overview

This paper provides a solution to evaluate the performance (missed alarm rate, false alarm rate and average detection delay) of alarm systems in which adaptive, dynamic or variable threshold is used.

Summary

Confusion matrix has long been used for performance evaluation of machine learning/deep learning-based classification. Deriving accuracy or other performance parameters through using estimated probability density functions can be more advanced and more reliable. However, for complex algorithms, it is not easy to find the separation line (threshold). But what if you can find it? Then you can use our approach to evaluate your ML/DL algorithm in a more advanced way (One step toward Safe AI).

Author Comments

koorosh aslansefat, PhD
koorosh aslansefat, PhD
University of Hull
Researcher
Hull, East Yorkshire | United Kingdom
Writing this article was a great pleasure as it has co-authors with whom I have had long-standing collaborations. This article also leads to alarm performance assessment groups contacting me and ultimately to greater involvement in alarm design research.koorosh aslansefat, PhD

Resources

https://www.sciencedirect.com/science/article/pii/S0019057819303519#d1e8243
https://www.sciencedirect.com/science/article/pii/S0019057819303519#d1e8243

Performance evaluation and design for variable threshold alarm systems through semi-Markov process.

Authors:
koorosh aslansefat, PhD
koorosh aslansefat, PhD
University of Hull
Researcher
Hull, East Yorkshire | United Kingdom

ISA Trans 2020 Feb 13;97:282-295. Epub 2019 Aug 13.

MAPNA Electric and Control Engineering and Manufacturing Company, Karaj, Iran. Electronic address:

In large industrial systems, alarm management is one of the most important issues to improve the safety and efficiency of systems in practice. Operators of such systems often have to deal with a numerous number of simultaneous alarms. Different kinds of thresholding or filtration are applied to decrease alarm nuisance and improve performance indices, such as Averaged Alarm Delay (ADD), Missed Alarm and False Alarm Rates (MAR and FAR). Among threshold-based approaches, variable thresholding methods are well-known for reducing the alarm nuisance and improving the performance of the alarm system. However, the literature suffers from the lack of an appropriate method to assess performance parameters of Variable Threshold Alarm Systems (VTASs). This study introduces two types of variable thresholding and proposes a novel approach for performance assessment of VTASs using Priority-AND gate and semi-Markov process. Application of semi-Markov process allows the proposed approach to consider industrial measurements with non-Gaussian distributions. In addition, the paper provides a genetic algorithm based optimized design process for optimal parameter setting to improve performance indices. The effectiveness of the proposed approach is illustrated via three numerical examples and through a comparison with previous studies.

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Source
http://dx.doi.org/10.1016/j.isatra.2019.08.015DOI Listing
February 2020
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