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Early failure detection of paper manufacturing machinery using nearest neighbor based feature extraction
  • Wonjae Lee,
  • Kangwon Seo
Wonjae Lee
University of Missouri

Corresponding Author:[email protected]

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Kangwon Seo
University of Missouri
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In a paper manufacturing system, it can be substantially important to detect machine failure before it occurs and take necessary maintenance actions to prevent a detrimental breakdown of the system. Multiple sensor data collected from a machine provides useful information on the system's health condition. However, it is hard to predict the system condition ahead of time due to the lack of clear ominous signs for future failures, a rare occurrence of failure events, and a wide range of sensor signals which might be correlated with each other. In this paper, we present two versions of feature extraction techniques based on the nearest neighbor combined with machine learning algorithms to detect a failure of the paper manufacturing machinery earlier than its occurrence from the multi-stream system monitoring data. First, for each sensor stream, the time series data is transformed into the binary form by extracting the class label of the nearest neighbor. We feed these transformed features into the decision tree classifier for the failure classification. Second, expanding the idea, the relative distance to the local nearest neighbor has been measured, results in the real-valued feature, and the support vector machine is used as a classifier. Our proposed algorithms are applied to the dataset provided by IISE 2019 data competition, and the results show the better performance than the given baseline.
04 May 2020Submitted to Engineering Reports
04 May 2020Submission Checks Completed
04 May 2020Assigned to Editor
05 May 2020Reviewer(s) Assigned
27 May 2020Editorial Decision: Revise Major
03 Jul 20201st Revision Received
07 Jul 2020Submission Checks Completed
07 Jul 2020Assigned to Editor
14 Jul 2020Reviewer(s) Assigned
18 Aug 2020Editorial Decision: Revise Major
31 Aug 20202nd Revision Received
31 Aug 2020Submission Checks Completed
31 Aug 2020Assigned to Editor
01 Sep 2020Editorial Decision: Accept
24 Sep 2020Published in Engineering Reports. 10.1002/eng2.12291