【作者】 王标；

【导师】 毛志忠；

【作者基本信息】 东北大学 ， 控制理论与控制工程， 2014， 硕士

【摘要】 随着科技的飞速发展,过程控制领域中对系统的要求越来越高,主要表现在控制精度、响应速度、稳定性以及鲁棒性等方面,因此导致实际中过程控制系统的规模越来越大,复杂程度越来越高。此外,在大多数情况下,由于工业过程或被控对象具有机理复杂性、非线性、参数时变性、大滞后、强耦合性、不确定性等特点,致使难以建立工业过程或被控对象精确的数学模型,同时也为控制决策的制定带来了很大的挑战。为此,科研人员将目光转向过程控制中产生的大量的过程数据,从过程数据出发,寻求基于数据的建模方法和控制策略,并取得了一定的研究成果,这使得过程数据在过程控制系统领域中的地位有了显著的提高。但是,随着过程数据重要性的不断提高,其质量的好坏也越来越受到科研人员的关注,因为一组良好的过程数据能为模型的建立以及控制决策的制定提供精确的依据。反之,含有异常值的过程数据将会使模型的建立不够准确,甚至导致过程控制系统中控制策略制定的失败。在此背景下,本文在深入分析了过程控制系统中过程数据特点的基础上,提出了针对过程控制系统的异常数据检测算法。主要内容归纳如下：(1)针对过程控制系统的结构和过程数据的产生机制,本文为过程控制系统中的异常数据给出了专门的定义,进而根据此定义制定出了异常数据的检测策略,即基于模型的检测。(2)在检测策略中至关重要的一步就是数据模型的建立。本文根据过程数据的特点提出了利用时间序列对过程数据进行建模的思想,在线性建模思想的基础上,采用了具有联想记忆功能的动态神经网络来对时间序列进行建模。通过仿真对比分析,动态神经网络对时间序列建模无论从准确性还是效率上都优于线性建模方法,仿真实验证明了本文采用的建模方法具有较好的应用效果。(3)根据建立好的动态神经网络模型,可以得到过程数据的拟合残差。为了更好的分析此残差。本文引入了小波变换的思想,通过对残差进行小波变换,再根据小波分析的模极大值原理和李氏指数等相关理论可以较好的检测出过程数据中存在的异常数据。同样通过仿真实验验证了小波分析在异常值检测方面的有效性。(4)针对在小波变换思想中存在的阈值选择问题。本文将隐马尔可夫模型(HMM)的思想理论应用到小波分析上。由于HMM模型是一个概率统计模型,可以通过对小波系数的分析直接判断出数据的异常与否,很好的避免了阈值的选择问题。仿真实验验证了本文方法的有效性和实用性。更多还原

【Abstract】 With the rapid development of technology, there are more and more needs for systems in the field of process control, which usually refer to control precision, response speed, stability and robustness. So the scale of process control systems in the real world has became bigger and bigger, and the complexity has became higher and higher. In addition, most industry processes have the following features:mechanism complexity, nonlinearity, time varying of parameters, big lag, strong coupling and so on, which bring great difficulties to modeling and bring a great challenge for making control strategy at the same time. For this reason, scientists have turned their sights to the process data of process control systems. They try to find the methods of modeling and control strategy based on the process data, and they have made some results, which has improved the position of process data in the field of process control. With the improvement of the process data, however, the quality of process data has been more welcomed than before. Because a set of data with higher quality will bring a accurate base to modeling and strategy making. On the other side, a set of data with lower quality will even result in the failure of control, which will make a terrible result.On this background, after analyzing the characteristics of process control systems and process data, this paper proposed a special method for outlier detection in the process control systems. Main research results are summarized as follows:(1) aiming at the structural characteristics of process control system and the application of process data, this paper gives a special definition for the outlier in process control system, and makes a detection strategy based on this definition, which refers to detection based on model.(2) modeling is a very important step in the detection strategy. This paper proposed that we can use time-series to model the process data according to the characteristics of process data. After analyzing traditional method, this paper proposed dynamic neural network which has the function of association and memory to model the time-series. After simulation, we can find that the dynamic neural network works better than the traditional method at the aspect of veracity and efficiency, which has proved that DNN has a better effect.(3) based on the DNN model, we can get the fitting residual error. This paper proposed thought of wavelet analysis for analyzing this fitting residual error more efficiently. After transforming fitting residual error with wavelet analysis, we can detect the outliers in the process data based on the theory. Also, after simulation we can find that wavelet analysis works efficiently at detecting outliers.(4) because of a problem about the wavelet analysis, which is threshold, this paper connected hidden Markov model with wavelet analysis. As HMM is a statistical model, it can detect the outliers directly after analyzing the wavelet coefficient. This connection solves the problem of threshold efficiently. And the practicality and effectiveness of this method has been proved through simulation experiments.更多还原