Causal Inference in Industrial Alarm Data by Timely Clustered Alarms and Transfer Entropy

Fahimipirehgalin, Mina; Weiss, Iris R.; Vogel‐Heuser, Birgit

doi:10.23919/ecc51009.2020.9143823

Cited by 12 publications

(2 citation statements)

References 17 publications

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“…low pressure and flow alarm of a pipe will further raise a low level alarm of the attached tank. Data-driven pattern recognition methods are applied to detect alarm sequences in historic alarm data to support operators in case of alarmfloods by identifying the root alarm [ 56 , 57 ]. However, detecting alarm sequences in complex systems with its chattering alarms and overlaying sequences is a challenging task.…”

Section: Towards Combining Data Analysis With Knowledge-based Systemsmentioning

confidence: 99%

Potential for combining semantics and data analysis in the context of digital twins

Vogel‐Heuser

Ocker

Weiss

et al. 2021

Phil. Trans. R. Soc. A.

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Modern production systems can benefit greatly from integrated and up-to-date digital representations. Their applications range from consistency checks during the design phase to smart manufacturing to maintenance support. Such digital twins not only require data, information and knowledge as inputs but can also be considered integrated models themselves. This paper provides an overview of data, information and knowledge typically available throughout the lifecycle of production systems and the variety of applications driven by data analysis, expert knowledge and knowledge-based systems. On this basis, we describe the potential for combining data analysis and knowledge-based systems in the context of production systems and describe two feasibility studies that demonstrate how knowledge-based systems can be created using data analysis. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

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Section: Towards Combining Data Analysis With Knowledge-based Systemsmentioning

confidence: 99%

Potential for combining semantics and data analysis in the context of digital twins

Vogel‐Heuser

Ocker

Weiss

et al. 2021

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Kinghorst et al [32] introduced a pre-processing step in the process of alarm flood analysis to enhance the robustness of the alarm system in dealing with the random alarm or interference alarm mode through probability calculation of alarm correlation. Fahimipirehgalin et al [33] proposed a data-driven method, using alarm log files to detect the causal sequence of alarms. In this method, an efficient alarm clustering method based on the time distance between alarms is proposed, which is helpful to preserve adjacent alarms in a cluster.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Alarm Reduction of Intrusion Detection System Based on Clustering and Whale Optimization Algorithm

Wang¹,

Gu²,

Tang³

2021

Applied Sciences

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With the frequent occurrence of network security events, the intrusion detection system will generate alarm and log records when monitoring the network environment in which a large number of log and alarm records are redundant, which brings great burden to the server storage and security personnel. How to reduce the redundant alarm records in network intrusion detection has always been the focus of researchers. In this paper, we propose a method using the whale optimization algorithm to deal with massive redundant alarms. Based on the alarm hierarchical clustering, we integrate the whale optimization algorithm into the process of generating alarm hierarchical clustering and optimizing the cluster center and put forward two versions of local hierarchical clustering and global hierarchical clustering, respectively. To verify the feasibility of the algorithm, we conducted experiments on the UNSW-NB15 data set; compared with the previous alarm clustering algorithms, the alarm clustering algorithm based on the whale optimization algorithm can generate higher quality clustering in a shorter time. The results show that the proposed algorithm can effectively reduce redundant alarms and reduce the load of IDS and staff.

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A review of alarm root cause analysis in process industries: Common methods, recent research status and challenges

Alinezhad

Roohi²,

Chen³

2022

Chemical Engineering Research and Design

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Causal Inference in Industrial Alarm Data by Timely Clustered Alarms and Transfer Entropy

Cited by 12 publications

References 17 publications

Potential for combining semantics and data analysis in the context of digital twins

Potential for combining semantics and data analysis in the context of digital twins

Research on Alarm Reduction of Intrusion Detection System Based on Clustering and Whale Optimization Algorithm

A review of alarm root cause analysis in process industries: Common methods, recent research status and challenges

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