基于复杂网络聚类的提升机主轴系统故障诊断

针对摩擦提升机主轴系统故障耦合、特征微弱且故障样本不易获得的问题,提出一种基于复杂网络聚类的故障诊断方法。该方法从故障数据表现出社团结构的本质出发,以各数据样本为节点,样本间相似度为有权边,构建加权无向复杂网络模型。将欧氏空间的距离概念推广到样本的相似性度量上提出广义Ward距离,并以此为划分准则,采用凝聚型合并过程实现网络模型中社团的聚类,即故障样本的模式识别。对主轴系统过载、滚动轴承元件故障及减速器齿轮磨损的分析结果表明,该方法能准确对已知故障类型数据进行聚类,且在过程中不预设类别数,为收集异常数据以便未知故障的发现与诊断提供了数据支持。与多元支持向量机与快速Newman算法的对比结果表明,该方法具有更高的识别精度与效率。

Fault Diagnosis for Spindle System of Hoist Based on Complex Network Clustering

The faults of the friction hoist spindle usually show characteristics of coupling, weak feature and less availability of samples. In light of these problems, a fault diagnosis method based on complex network clustering was proposed. Starting from the essence of the community structure, which was displayed by fault data, a weighted and undirected complex network model was constructed by nodes that abstracted from each sample and weighting edges that were represented by the similarity of samples. The generalized Ward distance, which was obtained by extending the concept of distance from Euclidean space to a similarity measurement, was proposed as a distinguishing criterion. Then, a clustering algorithm of a network model was developed by a hierarchical and agglomerative progress, namely, the pattern recognition of fault samples was accomplished. By analyzing the fault samples acquired from overload, elements failure of rolling bearing, and worn gear of the reducer, the experimental results indicate that the proposed method can effectively cluster fault samples of a known type and provide data support for collecting unusual samples, which can be used to discover and diagnose the unknown fault pattern, for the number of types was not preset. The proposed method is more accurate and productive than multi-class support machines and Fast Newman algorithm.