Identification and evaluation of alarm logs from the alarm management system

Urban, Petr; Landryová, Lenka

doi:10.1109/carpathiancc.2016.7501199

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…However, such studies are only meant for limited area and their application is not wide-ranging. Furthermore, there are studies [15][16][17][18][19] particularly dealing with the ergonomics of notification for alarm client; to enhance the capabilities of mobile HMI display for alarm notifications. However, such studies are unable to simultaneously support both iOS and Android platforms.…”

Section: Figure 2 Architecture Of Alarm System In Industrial Controlmentioning

confidence: 99%

“…There have been some studies carried out to emphasize the importance of designing an alarm system ergonomically as per the industrial standards. For example, in [15] and [16], the authors debate about designing an alarm system according to EEMUA standards and its configuration as per ISA norms. They analyzed the data from 5 different process systems.…”

Section: A State Of the Art Approachesmentioning

confidence: 99%

“…They analyzed them for their ergonomic design against a checklist of 148 items in 11 design areas like alarm presentation, alarm prioritization, documentation, training etc. Aforementioned studies [15][16][17][18] revealed that certain improvements are required in alarms system to meet the growing demands and standard of modern ICS.…”

Section: A State Of the Art Approachesmentioning

confidence: 99%

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A Model-Driven Alarms Framework (MAF) With Mobile Clients Support for Wide-Ranging Industrial Control Systems

et al. 2020

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An efficient and robust alarms management is an extremely desirable feature to prevent critical failures in modern Industrial Control Systems. Generally, alarm systems are categorized into two major components; i.e., Alarm Server and Alarm Client. Alarm server is responsible to process alarms on real time values, while alarm clients display the generated alarms. With the advent of modern technologies, like, Internet of Thing (IoT), the need for mobile alarm clients has significantly been increased, as alarms need to be displayed on different gadgets instead of traditional big screens. Owing to significance of alarms in the industry, several state-of-the-art approaches have been introduced, suggesting various improvements. However, such approaches are only meant for specific industry and cannot be applied across-the-board. Furthermore, full support for mobile clients is not available. On the other hand, certain industrial software products like, SIMATIC WinCC, Genesis64 provide complete alarm solution including mobile client features, as well. However, such solutions are proprietary with higher costs, i.e., pay per tag model. Considering such limitations, this article proposes a novel Model-driven Alarms Framework (MAF) with mobile clients (Android and iOS) support. Particularly, MAF comprises an Alarm Profile for Industrial Control Systems (APICS) for the modeling of server, mobile clients and configuration requirements of alarms with remarkable simplicity. Furthermore, a complete open source Alarms Profile Transformation Engine (APTE) has been implemented to automatically generate alarm server (Kotlin), native Android (Kotlin) and iOS (Swift) alarm clients from APICS-compliant models. The feasibility of proposed framework is demonstrated through two case studies (flour mill and home automation). The results prove that MAF not only provides complete alarm server solution to generate bulk of alarms simultaneously, but also supports android and iOS alarm clients for efficient visualization. Besides that, MAF is easy to use, cost-effective and can be applied in wide-ranging industrial applications.

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Section: Figure 2 Architecture Of Alarm System In Industrial Controlmentioning

confidence: 99%

Section: A State Of the Art Approachesmentioning

confidence: 99%

Section: A State Of the Art Approachesmentioning

confidence: 99%

See 1 more Smart Citation

A Model-Driven Alarms Framework (MAF) With Mobile Clients Support for Wide-Ranging Industrial Control Systems

et al. 2020

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“…In many cases the alarms occur faster than the human being is able to read them. The most common ways to handle alarm floods can be roughly grouped in one of the following approaches [5,7,26]:…”

Section: Current Statusmentioning

confidence: 99%

Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause

Wunderlich

Niggemann

2018

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

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In view of the increasing amount of information in the form of alarms, messages or also acoustic signals, the operators of systems are exposed to more workload and stress than ever before. We develop a concept for the reduction of alarm floods in industrial plants, in order to prevent the operators from being overwhelmed by this flood of information. The concept is based on two phases. On the one hand, a learning phase in which a causal model is learned and on the other hand an operating phase in which, with the help of the causal model, the root cause of the alarm sequence is diagnosed. For the causal model, a Bayesian network is used which maps the interrelations between the alarms. Based on this causal model the root cause of an alarm flood can be determined using inference. This not only helps the operator at work, but also increases the safety and speed of the repair. Additionally it saves money and reduces outage time. We implement, describe and evaluate the approach using a demonstrator of a manufacturing plant in the SmartFactoryOWL.

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“…Often it can be found that all alarms have the same priority, or sometimes a large percentage is assigned to one priority and only a little to other priorities. It is important to have alarms prioritized correctly because, in a setting where the operator Ec o p e t r ol receives a sequence of alarms in a short period, the priority is the only factor that the operator has to determine the alarm to which he must respond as priority [24]. Alarm management is a process whereby alarms are designed, monitored and managed to ensure more reliable and safe operations.…”

Section: Introduction 1 2 Theoretical Framementioning

confidence: 99%

An additional layer of protection through superalarms with diagnosis capability

Vásquez-Capacho

Pérez-Zúñiga

Muñoz

et al. 2020

CT&F Cienc. Tecnol. Futuro

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An alarm management methodology can be proposed as a discrete event sequence recognition problem where time patterns are used to identify the process safe condition, especially in the start-up and shutdown stages. Industrial plants, particularly in the petrochemical, energy, and chemical sectors, require a combined approach of all the events that can result in a catastrophic accident. This document introduces a new layer of protection (super-alarm) for industrial processes based on a diagnostic stage. Alarms and actions of the standard operating procedure are considered discrete events involved in sequences, where the diagnostic stage corresponds to the recognition of a special situation when these sequences occur. This is meant to provide operators with pertinent information regarding the normal or abnormal situations induced by the flow of alarms. Chronicles Based Alarm Management (CBAM) is the methodology used to build the chronicles that will permit to generate the super-alarms furthermore, a case study of the petrochemical sector using CBAM is presented to build the chronicles of the normal startup, abnormal start-up, and normal shutdown scenarios. Finally, the scenario validation is performed for an abnormal start-up, showing how a super-alarm is generated.

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Identification and evaluation of alarm logs from the alarm management system

Cited by 7 publications

References 4 publications

A Model-Driven Alarms Framework (MAF) With Mobile Clients Support for Wide-Ranging Industrial Control Systems

A Model-Driven Alarms Framework (MAF) With Mobile Clients Support for Wide-Ranging Industrial Control Systems

Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause

An additional layer of protection through superalarms with diagnosis capability

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