A STATISTICAL APPROACH FOR CONDITION MONITORING FOR MAINTENANCE MANAGEMENT IN RAILWAY

摘要：

This paper presents an approach for detecting and identifying faults in railway infrastructure components. The method is based on pattern recognition and data analysis algorithms. Principal Component Analysis (PCA) is employed to reduce the complexity of the data to two and three dimension. PCA involves a mathematical procedure that transforms a number of variables, which may be correlated, into a smaller set of uncorrelated variables called “principal components. In order to improve the results obtained, the signal has been filtered. The filtered has been carried out employing a State Space system model, estimated by Maximum Likelihood with the help of the well-known recursive algorithms Kalman Filter and Fixed Interval Smoothing. The models explored in this paper to analyse system data fits within the so called Unobserved Components class models.

作者: Fausto Pedro García Márquez

作者单位: ETSI Industriales, Universidad Castilla-La Mancha, Campus Universitario s/n, 13071 Ciudad Real, Spain

会议类型: 国际会议

会议名称: 第九届工程结构完整性国际会议(The Ninth International Conference on Engineering Structural Integrity Assessment)

会议地点: 北京

会议语种:英文

在线出版日期: 2007-10-15（万方平台首次上网日期，不代表论文的发表时间）