Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction

Büttner, Sebastian; Wunderlich, Paul; Heinz, Mario; Niggemann, Oliver; Röcker, Carsten

doi:10.1007/978-3-319-66808-6_6

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…This has not pointed out herein, as the goal has been to search for immediate causal relationships. Inferring Bayesian Networks are utilized in a different work [49]; this approach can also be useful towards a causal model [50]. However, Abele et al, in their own discussion, state the use of probabilities and simulations, while expert knowledge is needed, whereas, in the current work, knowledge use is solely used in the actions generation.…”

Section: Appl Sci 2020 10 X 12 Of 16mentioning

confidence: 99%

A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT

Papacharalampopoulos,

Giannoulis,

Stavropoulos

et al. 2020

Applied Sciences

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A dashboard application is proposed and developed to act as a Digital Twin that would indicate the Measured Value to be held accountable for any future failures. The current study describes a method for the exploitation of historical data that are related to production performance and aggregated from IoT, to eliciting the future behavior of the production, while indicating the measured values that are responsible for negative production performance, without training. The dashboard is implemented in the Java programming language, while information is stored into a Database that is aggregated by an Online Analytical Processing (OLAP) server. This achieves easy Key Performance Indicators (KPIs) visualization through the dashboard. Finally, indicative cases of a simulated transfer line are presented and numerical examples are given for validation and demonstration purposes. The need for human intervention is pointed out.

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Section: Appl Sci 2020 10 X 12 Of 16mentioning

confidence: 99%

A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT

Papacharalampopoulos,

Giannoulis,

Stavropoulos

et al. 2020

Applied Sciences

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“…Buẗtner web-based application to reduce alarm floods and identify the root cause of turmoil in a process, using machine learning concepts. 21 Their method uses the MMHC algorithm and maximum likelihood estimation and the results are delivered to operators with specifically designed human interface. The advantage of this method is that it can use the knowledge and expertise of plant and field experts, in addition to the automatic data analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Although the proposed method reduces the search region, it requires process data for implementation, which is a limitation. Büttner et al proposed a web-based application to reduce alarm floods and identify the root cause of turmoil in a process, using machine learning concepts . Their method uses the MMHC algorithm and maximum likelihood estimation and the results are delivered to operators with specifically designed human interface.…”

Section: Introductionmentioning

confidence: 99%

Preprocessing of Alarm Data for Data Mining

Mannani

Izadi

Ghadiri

2019

Ind. Eng. Chem. Res.

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Many industries, including process industry, face an increased number of alarms every day. This is due to advanced computer-based monitoring and control technologies that are widely available in all industrial plants. On the other hand, data mining, as a method of finding patterns in data, has been widely used to discover patterns and relationships in alarm data, in hopes of reducing the volume of alarms and operators’ workload. One of the first steps in data mining is to prepare and cleanup raw data for better mining, also known as preprocessing. In this paper, we focus on preprocessing of alarm data and investigate the steps required for data preparation. Two stepsnamely, removing chattering alarms and reconstruction of missing alarmsare more challenging. For chattering alarms, many algorithms are proposed with a discussion on the time frame that should be selected for removing chattering alarms. As for the reconstruction of missing alarms, two methods are presented, using information from the same alarm tag or other related alarms. A case study shows the efficiency of the proposed methods.

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Addressing Uncertainties in Complex Manufacturing Environments: A Multidisciplinary Approach

Dhiman

Plewe

Röcker

2018

Advances in Manufacturing, Production Management and Process Control

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Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction

Cited by 3 publications

References 35 publications

A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT

A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT

Preprocessing of Alarm Data for Data Mining

Addressing Uncertainties in Complex Manufacturing Environments: A Multidisciplinary Approach

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