Detecting Abrupt Changes Based on Dynamic Analysis of Similarity for Rotating Machinery Fault Prognosis

摘要：

Detecting abrupt changes of dynamic structure of mechanical systems by its condition-based time series data is an important basis for fault prognosis. Segmenting time series at abrupt change points can classify the different dynamic structures and determine when the underlying model has changed. A novel method based on the exponent dynamical cross-correlation factor is presented to detect abrupt change points. Ideal time series is used to evaluate the performance of the proposed method. CWRU vibration signal data analysis of bearings using the presented method show that the load changes have no significant effect on the dynamic characteristics and fault defects have strongly influence on dynamic characteristics of rotating machinery.

关键词： Prognostics the Exponent Dynamical Cross-correlation Factor Detecting abrupt change Rotating Machinery

作者: Jingjing Liu Shouqi Yuan Congli Mei Yue Tang Jianping Yuan

作者单位: Technical and Research Center of Fluid Machinery Engineering, Jiangsu University, Zhenjiang 212013 S Technical and Research Center of Fluid Machinery Engineering, Jiangsu University, Zhenjiang 212013 School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 3924-3927

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）