基于流形学习的机械设备早期故障诊断理论与关键技术研究

Aiming at characteristics of machinery early fault such as feature weak, feature coupling and dynamic development, a new idea and method based on manifold learning that posseses functions of multi-symptom domains feature fusion, decoupling, dynamic pattern recognition and newly added samples dynamic addition is proposed. Main contents include: ①researching on theoretical framework of “multi-symptom domains feature fusion →Feature compression with manifoldlearning→dynamic pattern recognition”, and constructing theoretical model based on this new mode for machinery early fault diagnosis; ②exploring new principle of weak fearture extacion based on manifold learning,and researching on structure and fusion Method of multi-symptom domains feature set, so as to comprehensive collecte and integrate early weark features of machinery; ③researching on decoupling method of strong coupling early fault feature,and introducing classification supervision mechanism and distance measure method, so as to effevtively decoupling and separate different early fault feature; ④ researching on dynamic pattern recognition model, and introducing mechanism of manifold space embedded intelligent decision, so as to dynamic track and accurate describe state variation of machinery; ⑤researching on mechanism of incremental learning, so as to solve newly added samples dynamic addition and adaptive learning and fusion of newly added samples and existing sample. This research project is not only greatly significant for improving level of condition monitoring and early fault diagnosis of machinery, but also highly valuable for ensuring safe, reliable and efficient running of the large-scale mechanisms.

针对机械设备早期故障征兆不明显、特征耦合、动态发展等特点，提出集多征兆特征融合、解耦、动态模式识别及新增样本动态添加等功能于一体的流形学习新思路、新方法。主要内容：①研究“多征兆域融合→特征约简→动态模式识别”的理论架构，构建基于新模式的机械设备早期故障诊断理论模型；②探索主流形学习的微弱特征提取新原理，研究多征兆域特征集构造和融合方法，实现对设备早期故障微弱特征的全面搜集和融汇；③研究强耦合早期故障特征解耦方法，引入分类监督机制和空间距离测度方法，实现对不同类型早期故障特征的有效解耦和分离；④进行动态模式识别模型研究，引入多流形空间嵌入智能决策和等距映射机制，实现对设备状态变化的动态跟踪和准确描述；⑤开展增量式学习机制研究，解决新增样本动态添加问题，实现新增样本与现有样本的自适应学习和融合。本项目的研究具有重要的理论意义，对于保障大型机械设备的安全、可靠、高效运行也具有重要的经济价值。