机械非线性多故障模式多源动态特征辨识及自适应诊断研究

项目名称： 机械非线性多故障模式多源动态特征辨识及自适应诊断研究

项目编号： No.61273176

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： 陈汉新

作者单位： 武汉工程大学

项目金额： 80万元

中文摘要： 机械非线性多故障模式多源动态特征辨识是故障诊断在流程工业生产线中应用所遇到的技术瓶颈和难题，它不仅提取单源故障信号时频特征, 而且要确保特征提取后非线性变量及多故障模式和多源故障特征在时间、频率和空间上的对应关系, 它的研究具有理论创新和实用价值及挑战性。针对全自动流程工业生产线安全监控与故障诊断的迫切需求，开展机械非线性多故障模式多源动态特征辨识及自适应诊断研究。分析非线性多故障模式下多源信号动态特征形成机理；研究多尺度平行因子分解理论，发展特征因子筛选优化算法，提高三维时频空特征信号分析精度；研究特征因子分解路径具有对应性和整体一致性的优化算法，调和特征辨识高效高精度与计算复杂性之间的矛盾；提出多源特征因子的三维时频空模型重构算法；研究基于多维动态特征提取辨识和决策算法理论的自适应诊断方法，促进机械故障诊断在流程工业生产过程中的应用，提高机械故障检测精准度和智能化水平。

中文关键词： 故障诊断；特征提取；多故障；非线性模式；多源信息

英文摘要： Multi-source dynamic feature extraction and recognition for the mechanical nonlinear multi-fault mode are the difficult technique and problem that the fault diagnosis is applied in the industrial process. It not only extracts the time-frequency features from the single signal, but also ensures the corresponding signal-frequency-space relations among the nonlinear variables, multi-fault modes and the faulty features from the multiple sources. The research is characteristic of the theoretical innovation and valuable applications with the challenges. In terms of the urgent requirements by the condition monitoring and fault diagnosis of the automatic industrial process, the research on the multi-source dynamic feature recognition for the nonlinear multi-fault mode and adaptive diagnosis is done. The mechanism about the generation of the dynamic features in the multiple sources under the nonlinear multi-fault modes is analyzed. The research on the theory about the multi-scale parallel factor decomposition is done. The optimization algorithm on the feature factor selection is developed. The analysis precision of the three-dimensional signal-frequency-space feature signal is improved. The research on the optimization algorithm about the decomposition routine of the feature factors with the characteristic correspondence

英文关键词： fault diagnosis；feature extraction；multi-fault；nonlinear mode；multi-source information