Applied RCM^2 Algorithms Based on Statistical Methods  被引量:1

Applied RCM^2 Algorithms Based on Statistical Methods

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作  者:Fausto Pedro García Márquez Diego J. Pedregal 

机构地区:[1]ETSI Industriales,Universidad de Castilla-La Mancha,Campus Universitario s/n 13071 Ciudad Real,Spain

出  处:《International Journal of Automation and computing》2007年第2期109-116,共8页国际自动化与计算杂志(英文版)

摘  要:The main purpose of this paper is to implement a system capable of detecting faults in railway point mechanisms. This is achieved by developing an algorithm that takes advantage of three empirical criteria simultaneously capable of detecting faults from records of measurements of force against time. The system is dynamic in several respects: the base reference data is computed using all the curves free from faults as they are encountered in the experimental data; the algorithm that uses the three criteria simultaneously may be applied in on-line situations as each new data point becomes available; and recursive algorithms are applied to filter noise from the raw data in an automatic way. Encouraging results are found in practice when the system is applied to a number of experiments carried out by an industrial sponsor.The main purpose of this paper is to implement a system capable of detecting faults in railway point mechanisms. This is achieved by developing an algorithm that takes advantage of three empirical criteria simultaneously capable of detecting faults from records of measurements of force against time. The system is dynamic in several respects: the base reference data is computed using all the curves free from faults as they are encountered in the experimental data; the algorithm that uses the three criteria simultaneously may be applied in on-line situations as each new data point becomes available; and recursive algorithms are applied to filter noise from the raw data in an automatic way. Encouraging results are found in practice when the system is applied to a number of experiments carried out by an industrial sponsor.

关 键 词:Maintenance railways state space models system reliability monitoring elements. 

分 类 号:TP301.6[自动化与计算机技术—计算机系统结构]

 

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