基于隐马尔科夫模型抗体的自组织免疫网络多通道故障诊断技术研究

项目名称： 基于隐马尔科夫模型抗体的自组织免疫网络多通道故障诊断技术研究

项目编号： No.51305086

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 机械、仪表工业

项目作者： 岳夏

作者单位： 广州大学

项目金额： 24万元

中文摘要： 随着我国产业升级的不断推进，故障诊断领域面临着在多通道、大数据条件下进行多故障诊断的问题。申请首先通过对数据聚类等数据挖掘技术对特征值的一维时间序列进行聚类；再采用隐马尔科夫模型对聚类后的典型动态过程建模，从而通过模型似然率输出得到故障动态趋势矢量。然后通过创新性地对人工免疫网络的亲和力定义进行扩展，将结构特性引入人工免疫网络，采用隐马尔科夫模型抗体链这一特定抗体组合的特定响应表征故障；最后通过隐马尔科夫模型抗体链的拓扑结构以及响应值编码矢量对故障进行诊断。本项研究结合了隐马尔科夫模型的动态建模能力以及人工免疫系统的自组织能力等优点，构建了混合智能诊断模型，通过故障动态趋势矢量建立了多通道信号与多故障之间的联系。项目的成功实施有望进一步拓展人工免疫系统的免疫响应方式与原理，构建针对智能制造装备等强调动态诊断的"多通道大数据信号--故障动态趋势矢量--多故障诊断"的故障诊断新理论与新方法。

中文关键词： 故障诊断；隐马尔科夫模型；信号缺失；最优观测序列；

英文摘要： With the continuous progress of China's industrial upgrading, fault diagnosis is faced with the problem of multiple fault diagnosis under the conditions of the large amount of data and multi-channel. This grant funds use clustering and other data mining techniques to cluster one-dimensional time sequence of the feature value and utilize hidden Markov model (HMM) to learn the typical dynamic samples. Thus, fault dynamic trend vector is composed of the likelihood outputs of the HMMs. Then, structural property is introduced into artificial immune network by the innovative extension of the affinity definition. Fault is characterized by HMM antibody chain which means the specific response of specific antibody combinations. At last, fault is diagnosed by the topology and response vector of HMM antibody chain. So, hybrid intelligent diagnostic model is constructed in this application, by combining dynamic modeling capabilities of HMM and self-organizing ability of AIS. And fault dynamic trend vector is applied to establish contact between multi-fault with the multi-channel signals. The success of this project will advance the immune theories of AIS, and is expected to build new theories and new methods for dynamic fault diagnosis, such as intelligent manufacturing equipment fault diagnosis, with structure of multi-chan

英文关键词： fault diagnosis；hidden Markov；Signal missing；Optimal observation sequence；