The rotor thermal deformation prediction is a challenging task due to the sophisticated processes involved in rotary air-preheater and limited hardware sensors, so it is necessary to employ a soft sensor model to get the accurate deformation value for the existing sealing technology. Additionally, aiming at the problems of low accuracy and underutilization of data of traditional methods, a novel soft sensor for air-preheater based on DBN-IPSO-SVR is proposed. The grey relational analysis (GRA) method is employed to provide reliable input variables for model training. The deep belief network (DBN) and the support vector regression (SVR) with the improved particle swarm optimization (IPSO) as data-driven model are employed to extract the features in the data. The results demonstrate the IPSO can obtain better parameters, the proposed soft sensor model significantly improved the performance of rotor thermal deformation prediction and is therefore a valuable non-contact measure tool for controlling air leakage.

由于旋转空气预热器中涉及的复杂过程和有限的硬件传感器，因此转子热变形预测是一项艰巨的任务，因此有必要采用软传感器模型来获取现有密封技术的准确变形值。此外，针对传统方法数据精度低和数据利用不足的问题，提出了一种基于DBN-IPSO-SVR的新型空气预热器软传感器。灰色关联分析（GRA）方法用于为模型训练提供可靠的输入变量。深度信念网络（DBN）和支持向量回归（SVR）以改进的粒子群优化（IPSO）作为数据驱动模型，用于提取数据中的特征。结果表明IPSO可以获得更好的参数，