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Data-driven Anomaly Detection and Forewarning Based on Grey Prediction Model

Published: 28 March 2022 Publication History

Abstract

Abstract: Among the endeavors towards high-quality development of manufacturing enterprises, the most fundamental bottom line is to ensure safety and prevent risks, and the data generated in manufacturing processes reflects potential risks in real time. Therefore, in this paper, through the case analysis of the time series data recorded by the equipment in the factory, the possible types of risks are obtained. The extent of deviation of exceptional value is acquired by fitting the normal data to indicate the degree of anomaly of the equipment. The paper proceeds to the building of a grey prediction model based on the model to predict the situation in the next hour, and then residual diagnostics and class ratio dispersion diagnostics are carried out to test the accuracy of that prediction, and the sensitivity analysis and overall evaluation on the prediction are made.

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EBIMCS '21: Proceedings of the 2021 4th International Conference on E-Business, Information Management and Computer Science
December 2021
539 pages
ISBN:9781450395687
DOI:10.1145/3511716
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 March 2022

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Author Tags

  1. Class ratio dispersion diagnostics
  2. Cubic spline interpolation
  3. Fitting
  4. Grey prediction
  5. Residual diagnostics

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EBIMCS 2021

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Overall Acceptance Rate 143 of 708 submissions, 20%

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