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

Koorosh Aslansefat¹, Mahdi Bahar Gogani², Sohag Kabir¹

¹School of Engineering and Computer Science, University of Hull, Kingston upon Hull, United Kingdom.

ISA Transactions

|August 21, 2019

Summary

This summary is machine-generated.

This study introduces a novel method for assessing Variable Threshold Alarm Systems (VTASs) using semi-Markov processes and Priority-AND gates. The approach optimizes alarm management for improved industrial safety and efficiency.

Keywords:

Alarm management Industrial alarm systems Optimal thresholding Performance assessment Semi-Markov Process Variable threshold alarm system

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Area of Science:

Industrial Engineering
Control Systems Engineering
Reliability Engineering

Background:

Effective alarm management is crucial for safety and efficiency in large industrial systems.
Operators face challenges with numerous simultaneous alarms, leading to alarm nuisance.
Existing methods like thresholding aim to reduce nuisance but lack robust performance assessment for variable threshold systems.

Purpose of the Study:

To introduce two novel variable thresholding techniques.
To propose a new performance assessment approach for Variable Threshold Alarm Systems (VTASs).
To develop an optimized design process for VTAS parameters.

Main Methods:

Utilized Priority-AND gate and semi-Markov process for VTAS performance assessment.
Incorporated semi-Markov process to handle non-Gaussian industrial measurements.
Employed a genetic algorithm for optimizing VTAS parameters.

Main Results:

The proposed approach effectively assesses VTAS performance, considering complex industrial data distributions.
Optimized parameter settings were achieved using a genetic algorithm, improving key performance indices.
Demonstrated the effectiveness through three numerical examples and comparison with prior studies.

Conclusions:

The novel performance assessment method enhances VTAS reliability and efficiency.
The integration of semi-Markov processes and genetic algorithms offers a robust solution for alarm management.
This work provides a valuable tool for improving safety in industrial operations.