基于SDG模型的故障诊断及应用研究
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摘要
在工业生产特别是流程工业中,在进行故障诊断研究时,存在着难以建立精确定量模型的困难,利用检测变量残差的方法进行故障诊断也存在许多困难。根据过程的作用机理和元素关系建立反映系统深层知识的定性模型,如符号定向图(SDG),然后与其它的定量方法相结合,研究基于SDG模型的半定量故障诊断方法,不失为一种新途径。然而,SDG模型的理论及其在流程工业中的应用还有很多问题有待解决,包括SDG的建模方法、推理机制、与定量算法的融合等。论文以流程工业的SDG模型建模和基于SDG模型的故障诊断方法为研究重点,主要研究四个问题:
(1)深入分析了SDG模型的特性,利用微分代数方程,论证了SDG模型是一个具有线性初始响应的定性模型,研究了流程工业SDG模型中由于负反馈和前馈导致的补偿响应和逆响应对基于SDG模型的故障诊断的影响,即其对相容路径的破坏,采用矩阵理论给出了SDG模型的数学描述。(2)研究了SDG模型的建立方法,论证了模型驱动的SDG节点信息和数据驱动的趋势信息的互补关系,采用趋势分析方法,利用关联节点趋势来建立SDG模型,并研究了基于趋势分析和SDG模型的定性故障诊断方法。(3)研究了基于SDG模型的半定量故障诊断方法,采用数据融合方法处理SDG模型及其样本之外的定量信息,一是利用模糊融合的方法,在故障逆向搜索过程中由节点的定量信息表示故障传播几率并进行半定量推理,二是利用信息融合的方法,融合初始响应的动态信息和最终响应的状态信息得到可能故障集;此外,在SDG模型数学描述的基础上采用Bayes理论研究了多源故障诊断。(4)将基于SDG模型的半定量故障诊断方法应用于“铝电解槽智能健康诊断系统”。
通过理论分析和实例与应用研究表明,论文对SDG模型的特性分析符合流程工业实际,所提出的SDG建模方法能够克服阈值敏感性,可建立较为准确的定性模型,基于SDG模型的模糊融合和信息融合半定量推理方法可以在推理过程中处理定量信息,提高了基于SDG模型的故障诊断的分辨率,从而建立了适于流程工业的基于SDG模型的故障诊断框架。
When fault diagnosis is studied in industry, especially in process industry, it is difficult to build precise quantitative models and diagnose faults by variable residual detection. According to function mechanism and unit relationships of the processes, qualitative models are built which reflect deep knowledge of systems, such as signed directed graph (SDG). Combined with other quantitative methods, SDG-based method is a new approach to study quasi-quantitative fault diagnosis. Yet there are many problems not resolved in the theory of SDG and its application in process industry, including modeling method, inference mechanism, fusion with quantitative methods, etc. The focuses of this dissertation are on the methods of SDG modeling in process industry and SDG-based fault diagnosis, and four problems are studied as below.
(1) The characters of SDG model are deeply analyzed. It is demonstrated that SDG is a linear qualitative model of initial response by differential algebraic equation. Then the influences of compensatory response and inverse response caused by negative feedback and feed forward in SDG model are analyzed, which destroy the consistent path. In addition, the mathematic description of SDG model by matrices is given. (2) SDG modeling method is studied. The complementary relationship of node information in model-driven SDG and trend information in data-driven trend analysis is illuminated. Then SDG model is built by trend analysis to associate node trends and meanwhile the qualitative fault diagnosis method based on trend analysis and SDG is proposed. (3) The method of quasi-quantitative fault diagnosis based on SDG is studied. Quantitative information not in SDG model and its sampling is processed by data fusion. One is to calculate fault propagation probabilities according to node quantitative information by fuzzy fusion in inverse inference. Another is to fuse dynamic information of initial response and state
(1)深入分析了SDG模型的特性,利用微分代数方程,论证了SDG模型是一个具有线性初始响应的定性模型,研究了流程工业SDG模型中由于负反馈和前馈导致的补偿响应和逆响应对基于SDG模型的故障诊断的影响,即其对相容路径的破坏,采用矩阵理论给出了SDG模型的数学描述。(2)研究了SDG模型的建立方法,论证了模型驱动的SDG节点信息和数据驱动的趋势信息的互补关系,采用趋势分析方法,利用关联节点趋势来建立SDG模型,并研究了基于趋势分析和SDG模型的定性故障诊断方法。(3)研究了基于SDG模型的半定量故障诊断方法,采用数据融合方法处理SDG模型及其样本之外的定量信息,一是利用模糊融合的方法,在故障逆向搜索过程中由节点的定量信息表示故障传播几率并进行半定量推理,二是利用信息融合的方法,融合初始响应的动态信息和最终响应的状态信息得到可能故障集;此外,在SDG模型数学描述的基础上采用Bayes理论研究了多源故障诊断。(4)将基于SDG模型的半定量故障诊断方法应用于“铝电解槽智能健康诊断系统”。
通过理论分析和实例与应用研究表明,论文对SDG模型的特性分析符合流程工业实际,所提出的SDG建模方法能够克服阈值敏感性,可建立较为准确的定性模型,基于SDG模型的模糊融合和信息融合半定量推理方法可以在推理过程中处理定量信息,提高了基于SDG模型的故障诊断的分辨率,从而建立了适于流程工业的基于SDG模型的故障诊断框架。
When fault diagnosis is studied in industry, especially in process industry, it is difficult to build precise quantitative models and diagnose faults by variable residual detection. According to function mechanism and unit relationships of the processes, qualitative models are built which reflect deep knowledge of systems, such as signed directed graph (SDG). Combined with other quantitative methods, SDG-based method is a new approach to study quasi-quantitative fault diagnosis. Yet there are many problems not resolved in the theory of SDG and its application in process industry, including modeling method, inference mechanism, fusion with quantitative methods, etc. The focuses of this dissertation are on the methods of SDG modeling in process industry and SDG-based fault diagnosis, and four problems are studied as below.
(1) The characters of SDG model are deeply analyzed. It is demonstrated that SDG is a linear qualitative model of initial response by differential algebraic equation. Then the influences of compensatory response and inverse response caused by negative feedback and feed forward in SDG model are analyzed, which destroy the consistent path. In addition, the mathematic description of SDG model by matrices is given. (2) SDG modeling method is studied. The complementary relationship of node information in model-driven SDG and trend information in data-driven trend analysis is illuminated. Then SDG model is built by trend analysis to associate node trends and meanwhile the qualitative fault diagnosis method based on trend analysis and SDG is proposed. (3) The method of quasi-quantitative fault diagnosis based on SDG is studied. Quantitative information not in SDG model and its sampling is processed by data fusion. One is to calculate fault propagation probabilities according to node quantitative information by fuzzy fusion in inverse inference. Another is to fuse dynamic information of initial response and state
引文
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