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Alarm sequence pattern analysis techniques for alarm prediction in automated manufacturing processes.

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Title: Alarm sequence pattern analysis techniques for alarm prediction in automated manufacturing processes.
Authors: Gao, Xinpu1 (AUTHOR), Kim, Namki2 (AUTHOR), Yang, Jeongsam1 (AUTHOR) jyang@ajou.ac.kr
Source: Journal of Mechanical Science & Technology. Feb2026, Vol. 40 Issue 2, p1393-1402. 10p.
Subjects: Sequential pattern mining, Bayes' theorem, Encoding, Fault diagnosis, Detection alarms, Outlier detection, Manufacturing process automation, Programmable controllers
Abstract: This study proposes an alarm sequence pattern analysis technique to detect and predict anomalies in automated manufacturing processes by utilizing programmable logic controller (PLC) alarm data. While traditional sensor-based anomaly-detection methods struggle to fully account for interactions between devices or temporal dependencies, this study analyzes complex alarm patterns across the entire process by considering both alarm event grouping and time interval encoding. Specifically, after data preprocessing, alarm events were grouped based on state transition or occurrence time criteria, and four experimental conditions were designed according to alarm event grouping and the inclusion or exclusion of time interval encoding. The generalized sequential pattern (GSP) algorithm was then applied to identify frequent alarm sequences, and a Bayesian probability-based approach was used to calculate the likelihood of subsequent alarm occurrences, thereby implementing a model to predict alarms with a high probability. The experimental results based on eight months of alarm logs (235332 records) from an automotive battery module assembly process demonstrated that the model incorporating state transition and time interval encoding achieved the highest prediction performance, with an F1 score of 89.1 %. This suggests that simultaneously considering alarm occurrence patterns and temporal relationships is effective in improving the prediction accuracy. The proposed technique is expected to be applicable to real-time prediction model development and integrated analysis with process variables in various industries. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 191693572
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PubType: Academic Journal
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  Data: Alarm sequence pattern analysis techniques for alarm prediction in automated manufacturing processes.
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  Data: <searchLink fieldCode="AR" term="%22Gao%2C+Xinpu%22">Gao, Xinpu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Namki%22">Kim, Namki</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Jeongsam%22">Yang, Jeongsam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jyang@ajou.ac.kr</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Mechanical+Science+%26+Technology%22">Journal of Mechanical Science & Technology</searchLink>. Feb2026, Vol. 40 Issue 2, p1393-1402. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Sequential+pattern+mining%22">Sequential pattern mining</searchLink><br /><searchLink fieldCode="DE" term="%22Bayes'+theorem%22">Bayes' theorem</searchLink><br /><searchLink fieldCode="DE" term="%22Encoding%22">Encoding</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+alarms%22">Detection alarms</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+process+automation%22">Manufacturing process automation</searchLink><br /><searchLink fieldCode="DE" term="%22Programmable+controllers%22">Programmable controllers</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study proposes an alarm sequence pattern analysis technique to detect and predict anomalies in automated manufacturing processes by utilizing programmable logic controller (PLC) alarm data. While traditional sensor-based anomaly-detection methods struggle to fully account for interactions between devices or temporal dependencies, this study analyzes complex alarm patterns across the entire process by considering both alarm event grouping and time interval encoding. Specifically, after data preprocessing, alarm events were grouped based on state transition or occurrence time criteria, and four experimental conditions were designed according to alarm event grouping and the inclusion or exclusion of time interval encoding. The generalized sequential pattern (GSP) algorithm was then applied to identify frequent alarm sequences, and a Bayesian probability-based approach was used to calculate the likelihood of subsequent alarm occurrences, thereby implementing a model to predict alarms with a high probability. The experimental results based on eight months of alarm logs (235332 records) from an automotive battery module assembly process demonstrated that the model incorporating state transition and time interval encoding achieved the highest prediction performance, with an F1 score of 89.1 %. This suggests that simultaneously considering alarm occurrence patterns and temporal relationships is effective in improving the prediction accuracy. The proposed technique is expected to be applicable to real-time prediction model development and integrated analysis with process variables in various industries. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1007/s12206-026-0153-9
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Bayes' theorem
        Type: general
      – SubjectFull: Encoding
        Type: general
      – SubjectFull: Fault diagnosis
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      – SubjectFull: Detection alarms
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      – SubjectFull: Outlier detection
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      – SubjectFull: Manufacturing process automation
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      – SubjectFull: Programmable controllers
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      – TitleFull: Alarm sequence pattern analysis techniques for alarm prediction in automated manufacturing processes.
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            NameFull: Gao, Xinpu
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            NameFull: Kim, Namki
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            NameFull: Yang, Jeongsam
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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