数据驱动论文：基于数据驱动的TE及FPSO生产流程故障诊断的研究.docVIP

数据驱动论文：基于数据驱动的TE及FPSO生产流程故障诊断的研究 【中文摘要】生产过程的故障诊断一直以来都是流程工业所重视的关键问题之一。深入开展工业过程故障诊断的研究和应用对保障运行安全和提高经济效益均有较大的促进作用。基于数据驱动的故障诊断研究是现在流程工业的一个重要关注点。本文分析了故障诊断的方法和故障诊断发展趋势，通过对主元分析（PrincipleComponent Analysis，PCA）故障诊断法原理以及Hotelling T2统计量、SPE统计量的分析，给出了其故障诊断的过程。同时，分析了几种常用核函数的特点以及核主元分析（Kernel Principle ComponentAnalysis，KPCA）故障检测算法的原理和及其应用过程。对田纳西伊斯曼（Tennessee-Eastman，TE）生产流程和正在实际生产运行的的浮式储油卸油生产系统（Floating Production Storage Offloading，FPSO）典型生产流程进行了分析。利用PID控制算法对TE生产流程建立了基于MATLAB的仿真。同时，对FPSO典型生产流程进行了仿真，最后获得了两个流程在正常生产状态和故障状态下的样本数据。将主元分析和核主元分析故障检测法在TE生产流程进行了算法的验证，然后在FPSO典型生产流程进行了故障诊断的应用。通过对主元故障检测法和核主元故障检测法检测效果和故障漏检率的对比分析，得出在具有强非线性环节的生产流程中核主元故障诊断方法具有一定的优势。最后本文分析了决策导向无环图（Decision Directed Acyclic Graph，DDAG）故障辨识方法，然后将其应用到TE生产流程和FPSO典型生产过程进行了故障的辨识。结果表明其DDAG故障辨识法可以满足两个流程故障辨识正确率和辨识时间的要求。 【英文摘要】The fault diagnosis of productive process is one of key problems in the process industry.Further study and application of fault diagnosis can make a great improvement on industrialprocess in aspect of keeping the working safety and raising the economic benefits. The faultdiagnosis based on data-driven has become a focal point in the process industry at present.The methods and the development trend of fault diagnosis are introduced in this paper.The process of fault diagnosis is taken by the research of principal component analysis (PCA)and two statistics. The two statistics consists of the Hotelling T2 statistic and SPE statistic. Atthe same time, some kernel functions are analyzed. The kernel principal component analysis(KPCA) is proposed. The steps of the fault detection method based on KPCA are also given.The Tennessee Eastman (TE) production process which can verify the fault diagnosisalgorithm and the Floating Production Storage Offloading (FPSO) production processwhich is running on the sea are analyzed and simulated. The simulation of TE productionprocess uses a PID control algorithm. The required data of fault diagnosis is obtained by thetwo simulations.The algorithms of fault detection by