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Application of randomized algorithms to the analysis and synthesis of model-based and data-driven fault diagnosis systems

随机算法在基于模型和数据驱动的故障诊断系统分析和综合中的应用

基本信息

批准号：
242153335
负责人：
Professor Dr.-Ing. Steven Xianchun Ding
金额：
--
依托单位：
Fachgebiet Automatisierungstechnik und komplexe Systeme (AKS)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/242153335?language=en
关键词：
Application randomized algorithms analysis synthesis

项目摘要

This research project deals with the analysis and synthesis of observer-based and data-driven fault detection systems (FD-systems). For our purpose, the so-called randomized Algorithms (RA) serves as the mathematical and systematic fundamentals of the study and investigation. The first objective of the project is to sketch a RA-based framework for the analysis and synthesis of observer-based FD-systems. On the basis of this framework, methods and algorithms will be developed for the computation of false alarm rate (FAR) and missed detection rate (MDR). Moreover, development of statistical learning methods for the optimization of observer-based FD-Systems in the context of the FAR and MDR will be addressed. Considering practical situations that the available process data may not cover all faulty operation scenarios, data-driven FD-methods may fail in these situations for a successful fault isolation and identification. It is the second objective of this project to develop methods and algorithms, which are based on the RA and used for generating fault data and further for identifying applicable fault models. These methods will be applied to the multivariate analysis (MVA) methods like PCA (Principle Component Analysis) and PLS (Partial Least Squares) and further to the data-driven observer-based FD-systems. The last objective of the project consists in developing, on the basis of the RA methods, an evaluation platform for the data-driven and model-based FD-systems, which is used for a comparable evaluation of FD-performance like FAR, MDR and detection time. The achieved theoretical results will be tested and demonstrated on the benchmark process Tennessee Eastman and on the laboratory system of heating exchange control. Finally, a software tool in the MATLAB-programming environment will be developed for the RA-based analysis and synthesis of observer-based FD-systems, and the FD-evaluation platform will be established with a web-access.

本研究项目涉及基于观测器和数据驱动的故障检测系统（fd -系统）的分析和综合。就我们的目的而言，所谓的随机算法（RA）是研究和调查的数学和系统基础。该项目的第一个目标是绘制一个基于ra的框架，用于分析和综合基于观察者的fd系统。在此框架的基础上，研究了虚警率（FAR）和漏检率（MDR）的计算方法和算法。此外，将讨论在FAR和MDR背景下优化基于观测器的fd系统的统计学习方法的发展。考虑到实际情况，可用的过程数据可能无法覆盖所有故障操作场景，数据驱动的fd方法可能在这些情况下无法成功地进行故障隔离和识别。本项目的第二个目标是开发基于RA的方法和算法，用于生成故障数据并进一步识别适用的故障模型。这些方法将应用于多变量分析（MVA）方法，如PCA（主成分分析）和PLS（偏最小二乘），并进一步应用于数据驱动的基于观测器的fd系统。该项目的最后一个目标是在RA方法的基础上，为数据驱动和基于模型的fd系统开发一个评估平台，用于对FAR、MDR和检测时间等fd性能进行可比评估。所取得的理论结果将在田纳西州伊士曼公司的基准工艺和热交换控制实验室系统上进行测试和论证。最后，在matlab编程环境下开发软件工具，对基于ra的观测器fd系统进行分析和综合，并建立基于web的fd评估平台。