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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering >Downcoiler surface fault prediction for a hot strip steel mill
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Downcoiler surface fault prediction for a hot strip steel mill

机译：热轧带钢卷取机表面故障预测

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摘要

This paper studies the means of detecting steel strip surface defects produced by a hot strip mill downcoiler. The analysis is based on the force feedback signals taken from the side-guide and the pinch-roll of the steel mill. The study compares the results from two approaches. One approach is to apply principal component analysis (PCA) on the autoregressive (AR) model spectrum, and the other is to apply PCA on the signal periodogram. The spectrum estimation processes help eliminate the noise effects, and the PCA then distinguishes the signals into different clusters. Some of these clusters will then represent the problematic strips. The analysis results on the data from China Steel Corporation (CSC) show that PCA in conjunction with the AR model is able to separate a major portion of the defective processes, and that PCA with the periodogram is not as effective. However, the two processes sometimes compensate for each other, and the combination of the methods may provide improved results.
机译：本文研究了一种检测热轧带钢下卷机产生的钢带表面缺陷的方法。该分析基于从钢厂的侧导板和夹送辊获得的力反馈信号。该研究比较了两种方法的结果。一种方法是在自回归（AR）模型频谱上应用主成分分析（PCA），另一种方法是在信号周期图上应用PCA。频谱估计过程有助于消除噪声影响，然后PCA将信号区分为不同的簇。这些集群中的一些集群将代表有问题的地带。来自中国钢铁公司（CSC）的数据的分析结果表明，PCA与AR模型结合使用能够分离出大部分缺陷工艺，而带有周期图的PCA效果不佳。但是，这两个过程有时会相互补偿，并且这些方法的组合可以提供改进的结果。

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