作　　者： ; ; ; ; ;

机构地区： 华南理工大学汽车摩擦学与故障诊断研究所

出　　处： 《华南理工大学学报（自然科学版）》 2012年第12期1-6,共6页

摘　　要： 针对基于能量耗损的齿轮故障模式识别问题,将监督学习与局部主成分分析结合,提出了一种改进的能有效提取数据低维流形结构与分类特征的局部线性嵌入算法.然后,分析了齿轮摩擦学系统能量耗损与能量耗损的故障模式识别方法.最后,以齿轮箱能量监测实验台为例,获取不同齿轮故障下输入能量耗损功率的变化,应用改进的局部线性嵌入算法进行故障的功率耗损降维与模式识别,通过多类支持向量机分类的准确率来判断分类的效果.研究表明,改进的局部线性嵌入算法有较高的识别率,是一种有效的齿轮能量耗损故障模式识别方法. This paper deals with the fault pattern recognition of gears based on energy loss. In the investigation, first, a modified LLE （Locally Linear Embedding） algorithm, which combines the supervised learning with the local principal component analysis, is proposed to effectively extract the low-dimension manifoht strueture and the classification feature of data. Then, the energy loss of gear tribological system and its fault pattern recognition me- thod are analyzed. Finally, by taking the test rig for energy loss monitoring of gear box as an example, the variations of input power loss under different kinds of gear faults are analyzed, the dimensionality reduction and pattern recog- nition are performed by using the modified LLE algorithm, and the classification performance of the algorithm is evaluated according to the recognition rate of the multi-class support vector machine. The results show that the modi- fied LLE algorithm is of high recognition rate and is effective in fault pattern recognition of gear energy loss.

关 键 词： 故障诊断 多故障分类 局部线性嵌入 流形学习 能量耗损 模式识别

领　　域： [机械工程] [机械工程]