【作者】 许亮；

【导师】 钱宇；

【作者基本信息】 华南理工大学 ， 计算机应用技术， 2007， 博士

【摘要】 流程工业系统庞大,流程复杂,不安全因素众多,异常事故时有发生。因此,安全运行是流程工业生产中的头等大事,而充分运用信息技术,确保生产安全,也一直是企业紧抓不放的重要技术课题。目前,随着DCS和计算机技术的广泛应用,大量蕴涵过程信息的数据被采集和保存,但是,这些数据并没有有效利用,因此,出现“数据丰富,信息匮乏”的现象。另一方面,长期的生产实践积累了大量的经验知识,尤其是非正常工况的处理经验,也没有得到很好运用。因此,如何将这些数据变为有用的信息,从中挖掘出影响过程安全运行的特征信息,进而利用这些信息并结合经验知识,提高流程工业的长期、稳定、安全运行是本文的研究重点。基于上述动机,本文提出过程安全运行智能支持系统的概念和框架,详细阐述了系统的结构、功能和关键技术。在此基础上,完成系统的开发,并应用到具体的工业流程。本文主要研究内容为:首先,针对化工过程的生产特点,在研究过程运行状态识别智能算法的基础上,结合过程模型、生产目标和智能技术,提出过程安全运行智能支持系统的理论框架。在讨论和分析系统关键技术的基础上,进而提出其核心技术-一种混合智能故障诊断方法。其次,提出一种基于分块特征向量选择的改进核主成分分析方法。建立核主成分分析模型必须计算核矩阵K,对于大样本集,K的计算很困难。本文利用分块特征向量选择方法,选择能够描述样本集特征的样本子集,然后用样本子集建立核主成分分析模型。实验表明,相比核主成分分析,二者性能没有明显不同,但该方法的核矩阵维数较少,简化了K的计算复杂度。第三,研究核特征提取和最小二乘支持向量机在化工过程运行模式分类中的应用。化工过程的数据通常具有高维数、高噪声、强非线性的特点,直接从这些数据中识别过程的运行模式是很困难的。利用核特征提取方法将输入数据进行分解,消除冗余或相关信息,提取数据中的过程特征信息,然后建立最小二乘支持向量机过程模式运行分类模型。利用获取的工业数据,对该方法进行了深入研究。实验结果表明了该方法的有效性。第四,提出一种融合先验知识的模糊最小二乘支持向量机分类方法。针对最小二乘支持向量机对噪声或孤立点敏感的问题,提出一种基于噪声分布模型和样本紧密度的模糊最小二乘支持向量机模型。在模型的训练过程中,通过引入噪声分布模型,融合样本数据的先验知识;为了区分数据和噪声,提出基于样本紧密度的策略。运用该策略以及噪声分布模型,自动生成相应样本数据点的权值,据此调整模糊LSSVM模型的模糊隶属度。实验结果表明该方法具有很好的抗噪声能力和鲁棒性。第五,提出一种过程故障诊断专家系统的设计方案。该方案着重解决了以下几方面的问题:在知识获取方面,采用故障树、经验知识表格和决策树方法有机结合,解决了专家系统知识获取的“瓶颈”问题;知识校验是知识获取的一个重要步骤,提出一种基于有向图的知识校验方法;知识库分为全局知识库和内存知识库,提出一种内存知识库的知识选择策略;为了消解知识冲突,融合过程知识,提出基于统计和时间序列分析的动态知识冲突消解策略。第六,基于以上研究,着重从实际应用对系统的要求出发,开发了过程安全运行智能支持系统,并详细描述了系统的结构和主要功能。通过在某石化公司润滑油生产过程的实际应用,验证了所开发系统的有效性和实用性,进而证明了本文提出的过程安全运行系统框架以及为实现该系统所采用的各种技术的实用性。更多还原

【Abstract】 Characteristics of process engineering are a complex flows and colossal system. There are many unsafe factors. Abnormal situations occur from time to time. Therefore, an operation safe is a most important thing for the process engineering. Using enough information technologies, it can assure that the production process is safe, and which it also is an important technology problem for the process engineering. At the present time, with the extending application of DCS (Distributed Computer System) and computer techniques, a great amount of process data which includes process information can be sampled and collected. But, these data are not employed availably, thus it leads abundant data and poor information. In other hand, an amount of empirical knowledge, especially treating abnormal situations, are accumulated. But, these information are not handled better. Therefore, it is research contents in the paper how to transform these data into informations which can be used, and extract feature information which effects on the operation safety of the process from the process data, and further make enough use of these information and combine the empirical knowledge to support the long, steady, and safe operation for the process engineering.Based on the motivation, a conception and frame for an intelligent aided system of process safety operation is presented in the paper. The structure, function, and some key techniques for the system are described in detail. The proposed system is developed and applied to a real industrial process.Firstly, according to the characteristic of chemical engineering, the intelligent aided system for process safety operation is presented based on an intelligent algorithm for identifying an operation state and combined with process models, production objects, and intelligent techniques. A hybrid intelligent method for fault diagnosis is presented based on the discussion and analysis for some key techniques.Secondly, an improved kernel principal component analysis (KPCA) based on multi-block feature vector selection is presented in the paper. A kernel matrix K need be computed in the process of constructing a model for KPCA. But, for a large sample, it is very difficult to solve the kernel matrix K. Using a method of multi-block feature vector selection, a sub-sample is selected to describe the all sample, then a model for KPCA is trained by the sub-sample. The experiment shows that performance of the proposed method and KPCA are almost equivalent. The dimension of kernel matrix K for the proposed method reduces. Consequently, the computing complexity of K drops.Thirdly, a classification method combining a kernel-based feature extraction with a least square support vector machines (LSSVM) for an operation mode of process is presented. A process data are characteristic of high dimension, strongly nonlinearity, high noise and small fault samples. Therefore, it is very difficult to directly identifying the process operation mode from these data. Using the kernel-based feature extraction, the inputting data are decomposed, and removed from redundant or related information, and extracted from process feature information. Then, the classification model based on LSSVM is built up for identifying the process operation mode. The real data from an industrial process is used to prove the proposed method. The experiment result shows that the proposed method is effective.Fourthly, a fuzzy least squares support vector machines classification method incorporated with a prior knowledge on data is presented. To address the drawback which a least squares support vector machines is sensitive to noises or outliers, the least squares support vector machines model combining with the prior knowledge on process data is represented based on a noise distribution model and a strategy based on a sample affinity. Information of noise distribution model for process data is introduced in the training process of the model;The strategy based on the sample affinity is presented to discriminate between data and noises. A fuzzy membership is automatically generated and assigned to each corresponding data point by using the strategy and the noise model. Thus, the fuzzy membership in the LSSVM model can be adjusted. The experiment result shows that the proposed method has better performance and robustness against the noise data.Fifthly, a fault diagnosis expert system for the chemical process is presented. The following problem is considered in the process of the design for the expert system. In the process of knowledge acquisition, a problem which a knowledge acquisition has always been the bottleneck in developing expert systems is solved by using a method combined a fault tree and an empirical knowledge table with a decision tress; A knowledge verification is an import step for the knowledge acquisition. The knowledge verification is carried out based on a directed graph approach in the paper; A selection strategy of knowledge rules in the memory knowledge base is presented; Incorporating process knowledge, a dynamical strategy of knowledge conflict resolution based on a statistics and a time-series analysis method is introduced for resolving a knowledge conflict.Sixthly, based on the previous research and a requirement of real application environment, the intelligent aided system for chemical process safety operation is developed. The structure and the main function for the proposed system are described in detail. The proposed system is applied to a lubricating oil process in a petroleum plant. The validity and practicability of the proposed system is proven. Further, the frame of the proposed system as well as some key techniques used to the system can be confirmed with the availability.更多还原