基于多视图加权聚类集成的高速列车工况识别

摘要

With the rapid development of China's high-speed train industry,some safety problems arising from the high-speed train operation are attracting more attention.Since the monitoring signals of the highspeed trains collected by sensors are nonlinear and non-stationary,it is difficult to identify the fault conditions of high-speed train.Therefore,in this paper,a multi-view clustering ensemble model based on weighted non-negative matrix factorization (WNMF) is proposed to it.Firstly,the vibration signals are analyzed the frequency domain,time-frequency domain and time domain.And the multi-views are obtained by extracting the eigenvector from the four aspects of the vibration signal,which are fast Fourier transform,wavelet packet energy,approximate entropy and fuzzy entropy of empirical mode decomposition,and the mechanical statistical characteristics.And then the clustering result of each view is obtained by the K-means.Secondly,two kinds of weight of the views are generated respectively by the contribution and the similarity of the clustering partitions.Finally,the output results of multiple clustering and the weights are combined for WNMF to ensemble.The experimental results show that the model can better identify fault conditions of high-speed trains.%随着中国高速列车行业的快速发展,高速列车运行所产生的安全隐患问题引发了更多的关注.由于利用传感器所采集到的高速列车监测数据具有非线性、非平稳的特点,导致故障工况难以识别,为此提出一种基于加权非负矩阵的多视图聚类集成模型(weighted non-negative matrix factorization,WNMF)来对车体走行部的故障工况进行识别.首先,对振动信号进行频域、时频域、时域的分析,通过快速傅里叶变换、小波包能量、经验模态分解的近似熵和模糊熵、机械统计特征四个方面提取特征向量,构建四个特征视图;其次进行K-means聚类,得到每个视图的结果;再通过聚类成员的贡献度和相似度分别求取各视图的两种权值;最后进行加权的非负矩阵分解集成.实验结果表明,该模型能够有效地识别高速列车的故障工况.