数据驱动的动态过程故障特征提取与模式分析

项目名称： 数据驱动的动态过程故障特征提取与模式分析

项目编号： No.61273167

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 宋执环

作者单位： 浙江大学

项目金额： 83万元

中文摘要： 面向动态工业过程监测，开展基于数据驱动的动态特征提取、故障模式分析和过程故障诊断理论及其应用研究。在动态多变量统计分析技术的基础上，提出基于多尺度和流形学习的动态数据特征提取方法；针对动态特性强烈的瞬态过程，提出基于子空间模型的故障检测和诊断方法，并在多尺度建模框架下，给出多模式瞬态过程的故障分析方法；建立动态过程的正常工况和异常工况模式库，利用实时数据库和模式分析技术，实现故障的在线监测和诊断；在复杂噪声环境下，建立基于贝叶斯统计分析的动态概率监测模型，并研究相应的故障重构、故障诊断和分类方法。本项目的研究成果对于化工、冶金、石化以及制药等典型工业生产过程的安全监测具有十分重要的理论意义和应用价值。

中文关键词： 故障诊断；过程监测；数据驱动；动态过程；故障特征提取

英文摘要： For monitoring of dynamic processes, this project intends to set a research work on data-driven dynamic informaiton extraction, fault pattern analysis, and process fault diagnosis theory and application aspects. Based on dynamic multivariate statistical analysis techniques, new information extraction methods which are constructed upon multiscale and manifold learning approaches are proposed. For a class of particular transition processes which have strong dynamic data behaviors, this project intends to develop a subspace model based method for fault detection and diagnosis, as well as a novel fault pattern analysis method under the multiscale modeling framework. For pattern analysis, the database for both of the normal and abnormal operation modes are constructed, online fault monitoring and diagnosis are then carried out with the incorporation of the real-time databased and pattern analysis technique. Besides, with the introduction of the Bayesian statistical analysis method, dynamic probabilistic monitoring models are constructed for processes under complicated noisy environments. Corresponding fault reconstruction, diagnosis and classification schemes are also developed based on the dynamic probabilistic modeling method. Achievements of this project will provide significant theoretical and application contrib

英文关键词： fault diagnosis；process monitoring；data-driven；dynamic process；fault feature extraction