面向过程监控的非线性特征提取方法研究
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摘要
随着现代科学技术的发展,流程工业的规模越来越庞大,过程工艺越来越复杂,自动化程度也越来越高。与此同时,人们对工业生产的安全问题、节能减排问题、产业结构优化问题的关注度也在不断提升。传统的过程监控、生产优化和在线测量方案很多是基于机理模型建立的,机理模型具有精确度高、可解释性强等优点,但在实际使用中,由于系统的复杂程度过高而难以获得严格、完整、科学的机理模型,加之测量数据不可避免存在着误差,系统也很难不受随机扰动和噪声的干扰,这些都极大地影响了机理模型准确性的发挥。另一方面,随着计算机科学技术的发展,人们可以比以往任何时候更便捷地获得和存储规模庞大的过程数据,进行比以往任何时候更复杂的科学计算和更快速的数据库检索,这一切都为基于“数据驱动”方式的建模、仿真和优化提供了硬件保障,而统计科学的发展也为之奠定了坚实的理论基础。
本文所关注的内容正是在这样的背景下,研究面向过程监控的非线性特征提取方法。文中所使用的术语“特征提取”是基于数据驱动的任务之一,如果研究对象是数据,则其目标为从原始的、繁芜复杂的数据中获取有价值的反应系统本质特征的信息,一般所获得的数据是原始信息的更低维表示；如果研究对象为输入输出的关系,则特征提取的结果是得到表征此种关系的模型。可见获取本质特征的过程既可看作对原系统进行建模,也可作为数据预处理步骤而存在,该步骤为后续建模提供更简约的数据集从而在降低模型复杂度的同时获得更精确的计算结果。具体而言,本文结合工业过程数据的特点,进行了以下几方面研究：
1.研究了经典的线性特征提取算法主成分分析法,以及极限学习机神经网络,用于故障诊断中的故障识别。
2.提出一种基于核映射的支持向量回归软测量模型,并应用于乙烯聚合反应过程的关键变量测量。
3.提出基于主曲线和多项式最小二乘的软测量算法,用于二氯乙烷精馏过程的关键流股含水量预测。
4.利用主曲线技术买现过程监控并在CSTR过程等仿真数据集上取得了理想的结果。
5.此外,本文还介绍了多种人工神经网络方法以及它们的若干典型应用,并将之作为和本文所提算法对比的参照。本文的创新性工作主要体现在：
1.用极限学习机分类器进行故障识别时,提出多路学习机表决策略,从而在不增加训练时间的前提下提高分类结果的稳定性。
2.用支持向量回归进行软测量预测建模时,通过粒子群优化算法估计得到相关模型参数,并对标准的PSO算法进行改进以降低陷入局部极值的风险,提出PSO-SVR模型。
3.建立了基于主曲线的非线性偏最小二乘模型,应用于软测量预测。在建模时引入相关性增强因子使得主曲线的提取过程能兼顾输入输出间的最大相关性,从而体现PLS的理念。
4.在主曲线用于监控的应用中,借鉴了多尺度主成分分析的做法,即对小波分解系数进行主曲线建模,提出了基于多分辨率分析的“多尺度主曲线”算法。
5.在主曲线用于监控的应用中,摈弃神经网络模拟映射关系的传统做法,而代之以插值计算,这样,既避免了神经网络的复杂计算和结果不确定性,又将运算负荷从偏重于建模阶段转变成建模和计算阶段分摊。
With the development of modern science and technology, the process industry is on a much larger scale and process technique is becoming more complex, also, the degree of automation becomes increasingly higher. Meanwhile, the problems of industrial safety, energy conservation, emission reduction, and industrial structure optimization are much concerned. Traditional process methods of such as monitoring, production optimization and online measurement are based on mechanism model, so they are highly accurate and interpretable. In practice, however, it is difficult to obtain rigorous mechanism models because of complexity in process system. And the system is subject to random disturbances, noise interference and measurement error. Such factors also affects the accuracy of mechanism models. With the development of computer science and technology, large numbers of process data can be easily obtained and stored, also more complex scientific computing and more rapid database retrieval can be done. These, with the addition of development of statistical science, provide basic foundations for data-driven modeling, simulation and optimization.
This thesis is carried out under the backgrounds mentioned above. It studies nonlinear feature extraction for process monitoring. Feature extraction is one of basic tasks of data-driven methods. If industial data are concerned, it intends to obtain the valuable information which describes the process essentially in complex original data. In general, the number of dimension extracted is lower than the original one. Feature extracting can also provide models which represent the relationship between inputs and outputs. Process of obtaining essential feature of industry can be regarded as either modeling of system or data preprocessing step which provide simpler data set for subsequent modeling, reducing the complexity of the model, and obtaining more precise calculation results. Taking into account the characteristics of industrial process data, the following aspects are studied in this thesis.
1. Classical linear feature extraction algorithm, principal component analysis, is combined with extreme learning machine classifier to obtain model for fault identification in fault diagnosis.
2. A novel soft-sensing method is proposed and applied to gas-phase ethylene polymerization process. Two important indexes representing the product characteristics, melt index and density, are calculated. The proposed method constructs a predictive model based on the kernel mapping support vector regression.
3. A soft-sensing algorithm based on principal curves and polynomial least squares is proposed and applied to predict water content of distillation unit of EDC.
4. Principal curve method is utilized for process monitoring, and it achieves desired results in CSTR and other simulated data.
5. Besides, this thesis introduces a few typical applications adopting the artificial neural networks as nonlinear feature extraction tools in process monitoring. These are taken as contrast against other nonlinear algorithms.
Some innovative achievements are obtained, such as:
1. While utilizing extreme learning machine classifier to identify faults, a voting strategy is taken to enhance the reliability of results.
2. Improved particle swarm optimization algorithm is utilized to estimate two parameters while using SVR to build soft-sensor and the PSO-SVR model is proposed.
3. While building models based on principal curves together with nonlinear polynomial least squares and utilizing them to soft-sening, correlation increment factor is designed and the nonlinear feature extracting can give consideration to maximizing relationship between inputs and outputs as in traditional PLS.
4. In the application of principal curves to process monitoring, multi-scale principal curve algorithm is proposed based on multi-resolution analysis. This algorithm uses wavelet coefficients to model principal curves just as in MSPCA. The experimental results are consistent with theoretical expectations and indicate that the algorithm is effective.
5. In the application of principal curves to process monitoring, neural network which used to simulate the mapping relationship can be replaced by interpolation. By doing so, the complexity computing of the NN and instability results can be avoided. The computing load is allocated to the stages of modeling and calculation evenly.
本文所关注的内容正是在这样的背景下,研究面向过程监控的非线性特征提取方法。文中所使用的术语“特征提取”是基于数据驱动的任务之一,如果研究对象是数据,则其目标为从原始的、繁芜复杂的数据中获取有价值的反应系统本质特征的信息,一般所获得的数据是原始信息的更低维表示；如果研究对象为输入输出的关系,则特征提取的结果是得到表征此种关系的模型。可见获取本质特征的过程既可看作对原系统进行建模,也可作为数据预处理步骤而存在,该步骤为后续建模提供更简约的数据集从而在降低模型复杂度的同时获得更精确的计算结果。具体而言,本文结合工业过程数据的特点,进行了以下几方面研究：
1.研究了经典的线性特征提取算法主成分分析法,以及极限学习机神经网络,用于故障诊断中的故障识别。
2.提出一种基于核映射的支持向量回归软测量模型,并应用于乙烯聚合反应过程的关键变量测量。
3.提出基于主曲线和多项式最小二乘的软测量算法,用于二氯乙烷精馏过程的关键流股含水量预测。
4.利用主曲线技术买现过程监控并在CSTR过程等仿真数据集上取得了理想的结果。
5.此外,本文还介绍了多种人工神经网络方法以及它们的若干典型应用,并将之作为和本文所提算法对比的参照。本文的创新性工作主要体现在：
1.用极限学习机分类器进行故障识别时,提出多路学习机表决策略,从而在不增加训练时间的前提下提高分类结果的稳定性。
2.用支持向量回归进行软测量预测建模时,通过粒子群优化算法估计得到相关模型参数,并对标准的PSO算法进行改进以降低陷入局部极值的风险,提出PSO-SVR模型。
3.建立了基于主曲线的非线性偏最小二乘模型,应用于软测量预测。在建模时引入相关性增强因子使得主曲线的提取过程能兼顾输入输出间的最大相关性,从而体现PLS的理念。
4.在主曲线用于监控的应用中,借鉴了多尺度主成分分析的做法,即对小波分解系数进行主曲线建模,提出了基于多分辨率分析的“多尺度主曲线”算法。
5.在主曲线用于监控的应用中,摈弃神经网络模拟映射关系的传统做法,而代之以插值计算,这样,既避免了神经网络的复杂计算和结果不确定性,又将运算负荷从偏重于建模阶段转变成建模和计算阶段分摊。
With the development of modern science and technology, the process industry is on a much larger scale and process technique is becoming more complex, also, the degree of automation becomes increasingly higher. Meanwhile, the problems of industrial safety, energy conservation, emission reduction, and industrial structure optimization are much concerned. Traditional process methods of such as monitoring, production optimization and online measurement are based on mechanism model, so they are highly accurate and interpretable. In practice, however, it is difficult to obtain rigorous mechanism models because of complexity in process system. And the system is subject to random disturbances, noise interference and measurement error. Such factors also affects the accuracy of mechanism models. With the development of computer science and technology, large numbers of process data can be easily obtained and stored, also more complex scientific computing and more rapid database retrieval can be done. These, with the addition of development of statistical science, provide basic foundations for data-driven modeling, simulation and optimization.
This thesis is carried out under the backgrounds mentioned above. It studies nonlinear feature extraction for process monitoring. Feature extraction is one of basic tasks of data-driven methods. If industial data are concerned, it intends to obtain the valuable information which describes the process essentially in complex original data. In general, the number of dimension extracted is lower than the original one. Feature extracting can also provide models which represent the relationship between inputs and outputs. Process of obtaining essential feature of industry can be regarded as either modeling of system or data preprocessing step which provide simpler data set for subsequent modeling, reducing the complexity of the model, and obtaining more precise calculation results. Taking into account the characteristics of industrial process data, the following aspects are studied in this thesis.
1. Classical linear feature extraction algorithm, principal component analysis, is combined with extreme learning machine classifier to obtain model for fault identification in fault diagnosis.
2. A novel soft-sensing method is proposed and applied to gas-phase ethylene polymerization process. Two important indexes representing the product characteristics, melt index and density, are calculated. The proposed method constructs a predictive model based on the kernel mapping support vector regression.
3. A soft-sensing algorithm based on principal curves and polynomial least squares is proposed and applied to predict water content of distillation unit of EDC.
4. Principal curve method is utilized for process monitoring, and it achieves desired results in CSTR and other simulated data.
5. Besides, this thesis introduces a few typical applications adopting the artificial neural networks as nonlinear feature extraction tools in process monitoring. These are taken as contrast against other nonlinear algorithms.
Some innovative achievements are obtained, such as:
1. While utilizing extreme learning machine classifier to identify faults, a voting strategy is taken to enhance the reliability of results.
2. Improved particle swarm optimization algorithm is utilized to estimate two parameters while using SVR to build soft-sensor and the PSO-SVR model is proposed.
3. While building models based on principal curves together with nonlinear polynomial least squares and utilizing them to soft-sening, correlation increment factor is designed and the nonlinear feature extracting can give consideration to maximizing relationship between inputs and outputs as in traditional PLS.
4. In the application of principal curves to process monitoring, multi-scale principal curve algorithm is proposed based on multi-resolution analysis. This algorithm uses wavelet coefficients to model principal curves just as in MSPCA. The experimental results are consistent with theoretical expectations and indicate that the algorithm is effective.
5. In the application of principal curves to process monitoring, neural network which used to simulate the mapping relationship can be replaced by interpolation. By doing so, the complexity computing of the NN and instability results can be avoided. The computing load is allocated to the stages of modeling and calculation evenly.
引文
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