The mechanical properties of hot rolled strip are the key index of product quality, and the soft sensing of them is an important decision basis for the control and optimization of hot rolling process. To solve the problem that it is difficult to measure the mechanical properties of hot rolled strip in time and accurately, a soft sensor based on ensemble local modeling was proposed. Firstly, outliers of process data are removed by local outlier factor. After standardization and transformation, normal data that can be used in the model are obtained. Next, in order to avoid redundant variables participating in modeling and reducing performance of models, feature selection was applied combing the mechanism of hot rolling process and mutual information among variables. Then, features of samples were extracted by supervised local preserving projection, and a prediction model was constructed by Gaussian process regression based on just-in-time learning (JITL). Other JITL-based models, such as support vector regression and gradient boosting regression tree models, keep all variables and make up for the lost information during dimension reduction. Finally, the soft sensor was developed by integrating individual models through stacking method. Superiority and reliability of proposed soft sensors were verified by actual process data from a real hot rolling process.

热轧带钢的力学性能是产品质量的关键指标，其软感知是热轧过程控制和优化的重要决策依据。针对热轧带钢力学性能难以及时、准确测量的问题，提出了一种基于集成局部建模的软传感器。首先，过程数据的异常值被局部异常值因子去除。经过标准化和变换后，就得到了模型可以使用的正态数据。其次，为了避免冗余变量参与建模和降低模型性能，结合热轧过程的机理和变量之间的互信息进行特征选择。然后，通过有监督的局部保留投影提取样本的特征，并通过基于即时学习（JITL）的高斯过程回归构建预测模型。其他基于 JITL 的模型，如支持向量回归和梯度提升回归树模型，保留所有变量并弥补降维过程中丢失的信息。最后，通过堆叠方法集成单个模型开发了软传感器。来自真实热轧过程的实际过程数据验证了所提出的软传感器的优越性和可靠性。软传感器是通过堆叠方法集成各个模型而开发的。来自真实热轧过程的实际过程数据验证了所提出的软传感器的优越性和可靠性。软传感器是通过堆叠方法集成各个模型而开发的。来自真实热轧过程的实际过程数据验证了所提出的软传感器的优越性和可靠性。