【作者】 葛志强；

【导师】 宋执环；

【作者基本信息】 浙江大学 ， 控制科学与工程， 2009， 博士

【摘要】 保证过程安全和提高产品质量是现代流程工业迫切需要解决的两个问题,作为过程系统工程领域的关键技术之一,过程监测技术是解决这两个问题的有效途径。由于集散控制系统(DCS)在流程工业中的广泛应用,极大地丰富了过程的数据信息,基于数据驱动的过程监测技术在过去十多年间得到了长足的发展。其中,多变量统计过程监测方法(MSPC)更是受到了学术界和工业界的普遍关注,已经成为过程监测领域的研究热点之一。但是,传统的MSPC方法对过程的限制条件较多,如过程数据必须服从高斯分布、线性、稳定单一工况等,本文在前人研究工作的基础上,针对不同的复杂工况过程,提出多种有效的过程监测方法,具体包括:(1)针对过程数据的复杂分布情况,提出一种基于独立成分分析和主元分析(ICA-PCA)两步信息提取策略的过程监测方法,并引入支持向量数据描述(SVDD)和因子分析(FA)方法对提出的方法进行了改进。其中,提出的基于SVDD重构的故障诊断方法解决了非高斯故障诊断的难点,是对重构类故障诊断方法的重要补充;提出的基于混合因子的故障识别方法也在很大程度上改善了故障识别的效果。(2)针对非线性工况过程,在基本的核主元分析(KPCA)方法的基础上,引入统计局部技术(LA)对其进行改进,新的方法消除了对过程数据分布的限制条件。为了简化传统非线性过程监测算法建模和在线实施的复杂性,提出了一种基于线性子空间集成和Bayesian推理的过程监测方法,不仅有效地提高了监测效果,而且在很大程度上降低了算法的复杂度。(3)针对过程的时变和多工况特性,并同时考虑到过程的复杂数据分布和非线性情况,提出了三种新的方法,即基于局部最小二乘回归(LSSVR)模型的方法、基于非线性外部分析的鲁棒方法以及基于二维线性子空间集成和Bayesian推理的方法。其中,基于局部模型的方法有效地改善了传统递归方法的不足,增强了时变过程监测的实时性;非线性外部分析鲁棒算法不仅有效地推广了外部分析方法在非线性过程中的应用,而且增强了该算法抗击噪声和离群点的能力;提出的二维监测方法不仅在很大程度上减弱了过程监测对知识和经验的依赖性,而且取得了很好的效果。(4)同时考虑过程动态性和非高斯性的监测方法研究目前还比较少,本文在子空间模型辨识方法的基础上,通过引入统计局部技术,提出一种基于SMILA的动态非高斯过程监测方法,相比已有的动态非高斯过程监测方法,新的方法获得了更满意的监测性能。(5)相比连续生产过程,间歇生产过程要复杂的多,由于目前对于多工况复杂间歇过程监测的研究还比较少,本文特别针对这种类型的间歇过程,提出一种基于Bayesian推理的监测方法,并将其推广至更为复杂的多阶段间歇生产过程中。最后,在总结全文的基础上,对过程监测领域的未来工作进行了展望。更多还原

【Abstract】 Process safety and product quality are two important issues that should be paid great attentions by modern industrial processes. As one of the key technologies in the process system engineering area, process monitoring can be employed to solve those two aspects. According to the wide use of the distribution control system in industrial processes, large amounts of data were collected, which have greatly accelerated the development of data-based process monitoring methods along the past decade. Particularly, the multivariate statistical process control (MSPC) method has gained greatly attentions both in industry and academy. and has become a hot spot in process monitoring area.However, the traditional MSPC method is limited to Gaussian, linear, stationary and single mode processes. Based on the existing research works, this dissertation proposes several efficient monitoring methods for different complex processes, which are summarized as follows.(1) According to the complex distribution of the process data, a two-step independent component analysis and principal component analysis (ICA-PCA) based information extraction strategy is proposed for process monitoring, which is sequently improved by support vector data description (SVDD) and factor analysis (FA). A new SVDD reconstruction based method is proposed to address the difficulty of non-Gausian fault diagnosis problem, which can be considered as a complement of reconstruction-based methods for fault diagnosis. Additionally, a mixed index is proposed for fault identification, which can also improve the performance of process monitoring.(2) For nonlinear process monitoring, the statistical local approach (LA) is introduced upon the traditional kernel PCA modeling structure, which can effectively eliminate the restriction of Gaussian distribution for the process data. Due to the offline modeling and online implementation difficulties of the existing methods, a new viewpoint is proposed for nonlinear process monitoring, which is based on linear subspace integration and Bayesian inference. Compared to the existing nonlinear methods, the new method can both improve the monitoring performance and reduce the algorithm complexity.(3) In order to improve the monitoring performance for time-varying and multimode processes, three new methods are proposed. A local least squares support vector regression (LSSVR) based method is proposed, which can greatly enhance the real-time performance of the method; A robust nonlinear external analyais is proposed, which extends the conventional linear external analysis to nonlinear processes, and simultaneously improves the robustness of the method to noises and outliers; A two-dimensional process monitoring method is also proposed, which greatly alleviates the lean of the monitoring method to process knowledge and experiences.(4) Due to the research status on non-Gaussian dynamic processes, few works have been reported. In this dissertation, a new monitoring method is proposed for these special processes, which is based on subspace model identification (SMI) and local approach (LA). In contrast to other methods, the new proposed SMILA method is more efficient in monitoring non-Gaussian dynamic processes.(5) The batch process is considered to be more complicated than continuous processes. Due to the lack of the monitoring research work on multimode batch process monitoring, a Bayesian inference based method is proposed for this special kind of processes, which is also extended for monitoring multiphase batch processes.Finally, Conlcusions and future research studies of the process monitoring ares are illustrated.更多还原