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An incipient on-line anomaly detection approach for the dynamic rolling process

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Abstract

Over the past decades, various methods have been developed to analysis and monitor the dynamic metal processes, specially the extensively used cold rolling process. However, some limitation still exists for the traditional data analysis tools to be implemented well for these processes. For example, the performance of many of the traditional data analysis approaches cannot be guaranteed when the distribution assumption is violated. Meanwhile, it is still lack of systematic method to make good use of the huge condition parameters. In this article we develop a viable on-line anomaly incipient detection technique towards the cold rolling process of steel sheets. Based on the condition-based SPC, the proposed approach can monitor the multi condition parameters as well as the corresponding output characteristic in a real-time manner simultaneously and efficiently. It provides a framework for statistical process monitoring development under such dynamic manufacturing environment in order to improve the detecting Sensitivity and Specificity. The real data practical application verifies that this proposed approach can have an excellent performance without the normal distribution assumption, thus it has great potential to be employed in a large application area.

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Abbreviations

μ:

mean

σ2:

variance

γ:

smoothing value

H:

control limit

H0:

initial value for H searching

nmodel:

total number of historical data sets

SSmax:

maximum value of the objective function

t:

index of sample number

pyt:

predicted output

ryt:

measured output

tyt:

target output

X:

vector containing the condition parameters

y:

output characteristic

d:

dimension number of condition parameters

Et:

statistic

ns:

number of sigma (σ) the variable is expected to be within

UCL:

Upper Control Limit

LCL:

Lower Control Limit

IC:

in-control

OC:

Out-of-control

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Author information

Authors and Affiliations

  1. Singapore Institute of Manufacturing Technology (SIMTech), 71 Nanyang Drive, Singapore, 638075, Singapore

    Yanjing Ou, Jinwen Hu & Xiang Li

  2. Department of Mechanical and Industrial Engineering, Northeastern University, 360 Huntington Avenue, Boston, 02115, United States

    Salah Haridy

Authors
  1. Yanjing Ou
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  2. Jinwen Hu
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  3. Xiang Li
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  4. Salah Haridy
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Correspondence to Yanjing Ou.

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Ou, Y., Hu, J., Li, X. et al. An incipient on-line anomaly detection approach for the dynamic rolling process. Int. J. Precis. Eng. Manuf. 15, 1855–1864 (2014). https://doi.org/10.1007/s12541-014-0539-y

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  • DOI: https://doi.org/10.1007/s12541-014-0539-y

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