地方特色大学“过程装备监控及诊断技术”课程教学改革探讨——以安徽建筑大学为例

"过程装备监控及诊断技术"在过程装备与控制工程(过控)专业课程体系中属于一门限定专业选修课,结合安徽建筑大学"依托建筑业、服务城镇化"的办学定位,本文在教学实践基础上,基于"卓越工程师教育培养计划"和学校的办学定位,分析了现阶段"过程装备监控及诊断技术"课程在课程支撑及教学内容体系、教材选用、教学模式、实践环节方面存在的问题,并探讨了可能改进的措施。以期为地方特色大学尤其是新建过控专业的高校"过程装备监控及诊断技术"课程的实施提供一定的参考。

基于核主元分析与多支持向量机的监控诊断方法及其应用

为保证密闭鼓风炉冶炼过程的正常运行,构造了一种基于核主元分析(KPCA)和多支持向量机(MSVM)的监控模型.该监控模型首先用核主元分析方法对过程数据进行特征提取,然后将代表过程特征的核主元送入到多支持向量机分类器中进行故障诊断与分类.仿真研究显示,该监控模型具有较好的泛化能力,能有效地应用于鼓风炉的监控诊断,可用于鼓风炉熔炼过程的现场操作指导.

Nonlinear online process monitoring and fault diagnosis of condenser based on kernel PCA plus FDA

A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.

Nonlinear Statistical Process Monitoring and Fault Detection Using Kernel ICA

A novel nonlinear process monitoring and fault detection method based on kernel independent component analysis(ICA) is proposed.The kernel ICA method is a two-phase algorithm:whitened kernel principal component(KPCA) plus ICA.KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel.ICA seeks the projection directions in the KPCA whitened space,making the distribution of the projected data as non-gaussian as possible.The application to the fluid catalytic cracking unit(FCCU) simulated process indicates that the proposed process monitoring method based on kernel ICA can effectively capture the nonlinear relationship in process variables.Its performance significantly outperforms monitoring method based on ICA or KPCA.

Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.

偏最小二乘及其扩展算法在石油化工生产中的应用

偏最小二乘(Partial Least Square or Projection to Latent Structures,简称PLS)是一种广泛应用的多元统计方法,可以处理高维、相关度高的海量数据,是一种多元线性回归方法。本文介绍了PLS的发展历史、算法原理并重点介绍了目前在石油化工生产过程上几个比较有代表性的应用领域,包括在石油化工产品中的化学计量学、石油炼制过程监控和故障诊断以及反应动力学和工艺优化。其中,化学计量学领域的应用已较为成熟,而过程监控和故障诊断领域与反应动力学和工艺优化领域则更多停留在实验室研究阶段,应用较少。最后对PLS在石油化工生产中的应用前景做了展望。