Time series methods applied to failure prediction and detection
作者： Fausto P García , Diego J Pedregal , Clive Roberts , None
DOI: 10.1016/J.RESS.2009.10.009
关键词: Time series approach 、 Operating cost 、 Moving average 、 Engineering 、 Critical point (mathematics) 、 Autoregressive–moving-average model 、 Regression analysis 、 Track (rail transport) 、 Simulation 、 Railway engineering 、 Algorithm
摘要: Abstract Point mechanisms are critical track elements on railway networks. A failure in a single point mechanism causes delays, increased operating costs and even fatal accidents. This paper describes the development of new robust automatic algorithm for detection mechanisms. Failures detected by comparing what can be considered ‘expected’ form signals predicted from historical records operation with those actually measured. The expected shape is forecast combination VARMA (vector auto-regressive moving-average) model harmonic regression model. has been tested large dataset taken an in-service at Abbotswood Junction UK. results show that faults detected.
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