Data-based hybrid tension estimation and fault diagnosis of cold rolling continuous annealing processes.

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-09-26 DOI:10.1109/TNN.2011.2167686
Qiang Liu, Tianyou Chai, Hong Wang, Si-Zhao Joe Qin
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引用次数: 18

Abstract

The continuous annealing process line (CAPL) of cold rolling is an important unit to improve the mechanical properties of steel strips in steel making. In continuous annealing processes, strip tension is an important factor, which indicates whether the line operates steadily. Abnormal tension profile distribution along the production line can lead to strip break and roll slippage. Therefore, it is essential to estimate the whole tension profile in order to prevent the occurrence of faults. However, in real annealing processes, only a limited number of strip tension sensors are installed along the machine direction. Since the effects of strip temperature, gas flow, bearing friction, strip inertia, and roll eccentricity can lead to nonlinear tension dynamics, it is difficult to apply the first-principles induced model to estimate the tension profile distribution. In this paper, a novel data-based hybrid tension estimation and fault diagnosis method is proposed to estimate the unmeasured tension between two neighboring rolls. The main model is established by an observer-based method using a limited number of measured tensions, speeds, and currents of each roll, where the tension error compensation model is designed by applying neural networks principal component regression. The corresponding tension fault diagnosis method is designed using the estimated tensions. Finally, the proposed tension estimation and fault diagnosis method was applied to a real CAPL in a steel-making company, demonstrating the effectiveness of the proposed method.

基于数据的冷轧连续退火过程混合张力估计与故障诊断。
冷轧连续退火生产线(CAPL)是提高带钢力学性能的重要装置。在连续退火过程中,带钢张力是一个重要的因素,它标志着生产线是否稳定运行。生产线上张力分布异常会导致带钢断裂和轧辊打滑。因此,为了防止故障的发生,有必要对整个张力剖面进行估计。然而,在实际退火过程中,沿机器方向只安装有限数量的带钢张力传感器。由于带钢温度、气流、轴承摩擦、带钢惯性和轧辊偏心的影响会导致非线性张力动力学,因此很难应用第一性原理诱导模型来估计张力分布。本文提出了一种基于数据的张力估计与故障诊断混合方法,用于估计相邻两轧辊之间的未测张力。采用基于观测器的方法,利用有限数量的张力、速度和每卷电流的测量值建立主模型,其中张力误差补偿模型采用神经网络主成分回归设计。利用预估张力设计了相应的张力故障诊断方法。最后,将所提出的张力估计和故障诊断方法应用于某炼钢公司的实际CAPL,验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE transactions on neural networks
IEEE transactions on neural networks 工程技术-工程:电子与电气
自引率
0.00%
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2
审稿时长
8.7 months
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