Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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Condition Monitoring and Diagnosis of a Hot Strip Roughing Mill Using an Autoencoder

오토인코더를 이용한 열간 조압연설비 상태모니터링과 진단

  • Received : 2019.02.07
  • Accepted : 2019.02.25
  • Published : 2019.03.31
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Abstract

Purpose: It is essential for the steel industry to produce steel products without unexpected downtime to reduce costs and produce high quality products. A hot strip rolling mill consists of many mechanical and electrical units. In condition monitoring and diagnosis, various units could fail for unknown reasons. Methods: In this study, we propose an effective method to detect units with abnormal status early to minimize system downtime. The early warning problem with various units was first defined. An autoencoder was modeled to detect abnormal states. An application of the proposed method was also implemented in a simulated field-data analysis. Results: We can compare images of original data and reconstructed images, as well as visually identify differences between original and reconstruction images. We confirmed that normal and abnormal states can be distinguished by reconstruction error of autoencoder. Experimental results show the possibility of prediction due to the increase of reconstruction error from just before equipment failure. Conclusion: In this paper, hot strip roughing mill monitoring method using autoencoder is proposed and experiments are performed to study the benefit of the autoencoder.

Keywords

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Figure 1. A Schematic illustration of hot strip rolling mill process

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Figure 2. Illustration and image(left) and picture of a roughing mill(right)

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Figure 3. Drive and non-drive side vibration(Top : Drive side, Bottom : Non Drive side)

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Figure 4. Scatter plots of evaluation data set(Normal: solid dots, Abnormal: empty dots)

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Figure 5. Data structures

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Figure 6. Autoencoder(right) and stacked autoencoder(left) structures

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Figure 7. Reconstruction error trend of autoencoder model

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Figure 8. Reconstruction error plot

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Figure 9. Original and reconstruction images

Table 1. Hyper-parameter for autoencoder modeling

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Table 2. Stacked autoencoder modeling

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