中文摘要：

从故障诊断的模式识别本质出发,利用网络表示故障数据结构,通过网络结构反映故障状态及其特征,把故障诊断聚类问题建模为子网络探测问题,提出基于网络结构分析的故障诊断策略。为了解决子网络划分中数据间相似度测度和划分测度设计这两个重要问题,引入复杂网络社群结构分析中的模块性概念,设计状态区分准则函数,并采用自底向上模块合并层次过程优化准则函数实现故障状态聚类,提出一种基于模块合并的故障诊断聚类算法。通过算法在标准数据集分类和真实压缩机故障系统诊断上的应用,分析相似度测度对算法的影响并验证了算法的性能。试验结果表明,与遗传算法,人工免疫网络等人工智能诊断方法相比,本文提出的算法能以较少的计算耗时,有效提取故障特征,获得理想的诊断正确率。

英文摘要：

Fault diagnosis, whose essence is pattern recognition of object’s operation state, can be accomplished through clustering methods. The network model is used to represent the fault data structure and thus the clustering problem is converted into the detection task for sub-network structures. Thereby, a fault diagnosis strategy based on complex network structure analysis is proposed. Corresponding to the two central issues for sub-network partition：similarity measure between samples and partition criteria, the modularity concept used broadly in the analysis of community structures in a complex network is introduced into the design of a states differentiating criterion function. To optimize this criterion and accordingly classify fault states, an agglomerative hierarchical clustering algorithm is developed. In applications such as benchmark data classification and four-stage piston compressor diagnosis problem, the effect of similarity measure on algorithm is discussed and the algorithm performance is testified. The comparative results with several artificial intelligent diagnosis algorithms show that the new algorithm can achieve higher diagnosis accuracy, and it is more straightforward, able to extract critical features of the data samples more accurately and therefore accomplishes data clustering with less computational cost.