软测量建模方法研究与应用
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摘要
软测量技术是当前过程控制领域研究的热点之一。本论文以实际工业过程为背景,探讨了软测量建模技术若干问题及解决方法。主要研究工作如下:
1.虽然目前存在着多种数据驱动软测量建模技术,这些方法针对其文献中的应用案例建模效果良好,但在更广泛的工程应用中被发现有着各自的不足,这是因为数据驱动建模技术的应用效果取决于该技术的假设是否符合对象的实际特性,即是否符合该对象的学习样本集所蕴含的信息特性,如噪声水平、非线性程度、采样的离散程度、工况点数量、数据波动程度等特性。本文将通过几组实际的工业对象数据,研究不同样本特性对各种常用数据驱动软测量建模技术的影响并分析其原因,得到一系列规律性结论,可用于指导数据驱动软测量建模技术的应用。
2.针对连续流程工业软测量对象普遍存在多工况点的特性,提出了一种基于仿射传播聚类、高斯过程和贝叶斯决策的多模型软测量建模方法。该方法通过仿射传播聚类将学习样本按照工况点进行聚类,利用高斯过程对每个子聚类建立软测量子模型,最后通过贝叶斯决策方法实现模型的联合估计概率化输出。该建模方法在硫回收过程在线质量监控中取得了良好的应用效果。
3.自适应观测器可以对未知状态和未知参数进行联合估计,是软测量建模的一种新方法。针对目前的线性自适应观测器没有考虑状态方程和输出方程同时含有未知参数的情况,设计了一个全局收敛的自适应观测器,并构造了其带有指数遗忘因子的形式来提高其抗干扰能力。数值仿真结果表明该自适应观测器具有快速收敛、抗干扰等期望的性能。
4.基于高增益观测器和自适应观测器理论,针对状态方程和输出方程同时含有待估计参数的一类非线性系统,设计了一种全局收敛的自适应观测器。仿真表明,该自适应观测器可以快速跟踪未知参数的变化。
5.针对汽车慢主动悬架系统在经过长时间使用后,主要零件容易老化的问题,将零件的老化程度作为状态方程中的待估计参数,建立全车悬架系统的状态方程,设计自适应观测器以实现零件老化系数的实时评估。数值仿真表明,该自适应观测器能够迅速估计零件的老化程度。
Soft sensor technology is one of the most important research directions in area of process control. In this dissertation, several issues and the corresponding solutions about soft sensor technology are discussed based on the real industrial process and the main contributions are described as follows.
1. Many soft sensor methods are introduced in literatures of science and show good performance in their special applications, but in the more wide range of practice, drawbacks of these methods appeared. In order to study on the scope of application, we investigate frequently-used soft sensing methods based on several industry applications and get some useful conclusions.
2. A multi-model soft sensing method based on Affinity Propagation, Gaussian process and Bayesian committee machine is presented. It uses Affinity Propagation clustering arithmetic to cluster training samples according to their work modes. Then, the sub models are estimated by Gaussian process regression. Finally, in order to get a global probabilistic prediction, Bayesian committee machine is adopted to combine the outputs of the sub estimators. The proposed method have been applied to predict H_2S and SO_2 concentrations of sulfur recovery unit. Practical applications indicate it is useful for the online prediction of quality specifications in industry processes.
3. An adaptive observer is a recursive algorithm for joint state-parameter estimation of parameterized state space systems. Previous works on globally convergent adaptive observers consider unknown parameters either in state equations or in output equations, but not in both of them. In this paper, a new adaptive observer is designed for linear time varying systems with unknown parameters in both state and output equations. Its global convergence for simultaneous estimation of states and parameters is formally established under appropriate assumptions. A numerical example is presented to illustrate the performance of this adaptive observer.
4. Based on the techniques of high gain observer and adaptive estimation theory, an adaptive observer is proposed for state fault and sensor fault estimation in a class of uniformly observable nonlinear systems. It is first assumed that a high gain observer exists for the fault-free system. With a parametric model of sensor fault, a high gain adaptive observer is designed for fault estimation. In order to establish the global convergence of the adaptive observer, in addition to the usual conditions for high gain observer convergence, a persistent excitation condition is also required, like in most recursive parameter estimation problems.
5. A full vehicle active suspension system is considered with the dynamics of the four actuators. The aging coefficients of suspension system componets are modeling as unknown time varying parameters. An adaptive observer is designed to estimate the aging coefficients. Simulation result shows the aging coefficients could be estimated rapidly.
1.虽然目前存在着多种数据驱动软测量建模技术,这些方法针对其文献中的应用案例建模效果良好,但在更广泛的工程应用中被发现有着各自的不足,这是因为数据驱动建模技术的应用效果取决于该技术的假设是否符合对象的实际特性,即是否符合该对象的学习样本集所蕴含的信息特性,如噪声水平、非线性程度、采样的离散程度、工况点数量、数据波动程度等特性。本文将通过几组实际的工业对象数据,研究不同样本特性对各种常用数据驱动软测量建模技术的影响并分析其原因,得到一系列规律性结论,可用于指导数据驱动软测量建模技术的应用。
2.针对连续流程工业软测量对象普遍存在多工况点的特性,提出了一种基于仿射传播聚类、高斯过程和贝叶斯决策的多模型软测量建模方法。该方法通过仿射传播聚类将学习样本按照工况点进行聚类,利用高斯过程对每个子聚类建立软测量子模型,最后通过贝叶斯决策方法实现模型的联合估计概率化输出。该建模方法在硫回收过程在线质量监控中取得了良好的应用效果。
3.自适应观测器可以对未知状态和未知参数进行联合估计,是软测量建模的一种新方法。针对目前的线性自适应观测器没有考虑状态方程和输出方程同时含有未知参数的情况,设计了一个全局收敛的自适应观测器,并构造了其带有指数遗忘因子的形式来提高其抗干扰能力。数值仿真结果表明该自适应观测器具有快速收敛、抗干扰等期望的性能。
4.基于高增益观测器和自适应观测器理论,针对状态方程和输出方程同时含有待估计参数的一类非线性系统,设计了一种全局收敛的自适应观测器。仿真表明,该自适应观测器可以快速跟踪未知参数的变化。
5.针对汽车慢主动悬架系统在经过长时间使用后,主要零件容易老化的问题,将零件的老化程度作为状态方程中的待估计参数,建立全车悬架系统的状态方程,设计自适应观测器以实现零件老化系数的实时评估。数值仿真表明,该自适应观测器能够迅速估计零件的老化程度。
Soft sensor technology is one of the most important research directions in area of process control. In this dissertation, several issues and the corresponding solutions about soft sensor technology are discussed based on the real industrial process and the main contributions are described as follows.
1. Many soft sensor methods are introduced in literatures of science and show good performance in their special applications, but in the more wide range of practice, drawbacks of these methods appeared. In order to study on the scope of application, we investigate frequently-used soft sensing methods based on several industry applications and get some useful conclusions.
2. A multi-model soft sensing method based on Affinity Propagation, Gaussian process and Bayesian committee machine is presented. It uses Affinity Propagation clustering arithmetic to cluster training samples according to their work modes. Then, the sub models are estimated by Gaussian process regression. Finally, in order to get a global probabilistic prediction, Bayesian committee machine is adopted to combine the outputs of the sub estimators. The proposed method have been applied to predict H_2S and SO_2 concentrations of sulfur recovery unit. Practical applications indicate it is useful for the online prediction of quality specifications in industry processes.
3. An adaptive observer is a recursive algorithm for joint state-parameter estimation of parameterized state space systems. Previous works on globally convergent adaptive observers consider unknown parameters either in state equations or in output equations, but not in both of them. In this paper, a new adaptive observer is designed for linear time varying systems with unknown parameters in both state and output equations. Its global convergence for simultaneous estimation of states and parameters is formally established under appropriate assumptions. A numerical example is presented to illustrate the performance of this adaptive observer.
4. Based on the techniques of high gain observer and adaptive estimation theory, an adaptive observer is proposed for state fault and sensor fault estimation in a class of uniformly observable nonlinear systems. It is first assumed that a high gain observer exists for the fault-free system. With a parametric model of sensor fault, a high gain adaptive observer is designed for fault estimation. In order to establish the global convergence of the adaptive observer, in addition to the usual conditions for high gain observer convergence, a persistent excitation condition is also required, like in most recursive parameter estimation problems.
5. A full vehicle active suspension system is considered with the dynamics of the four actuators. The aging coefficients of suspension system componets are modeling as unknown time varying parameters. An adaptive observer is designed to estimate the aging coefficients. Simulation result shows the aging coefficients could be estimated rapidly.
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