Time series methods applied to failure prediction and detection

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Time series methods applied to failure prediction and detection. / Garcia, FP; Pedregal, DJ; Roberts, Clive.

In: Reliability Engineering and System Safety, Vol. 95, No. 6, 01.06.2010, p. 698-703.

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@article{2069fd98d9d04a1aa4a52803c4b316ff,
title = "Time series methods applied to failure prediction and detection",
abstract = "Point mechanisms are critical track elements on railway networks. A failure in a single point mechanism causes delays, increased railway operating costs and even fatal accidents. This paper describes the development of a new robust and automatic algorithm for failure detection of point mechanisms. Failures are detected by comparing what can be considered the 'expected' form of signals predicted from historical records of point mechanism operation with those actually measured. The expected shape is a forecast from a combination of a VARMA (vector auto-regressive moving-average) model and a harmonic regression model. The algorithm has been tested on a large dataset taken from an in-service point mechanism at Abbotswood Junction in the UK. The results show that the faults can be predicted and detected. (C) 2010 Elsevier Ltd. All rights reserved.",
keywords = "Safety, Failure diagnostic, Railway engineering, Maintenance",
author = "FP Garcia and DJ Pedregal and Clive Roberts",
year = "2010",
month = jun,
day = "1",
doi = "10.1016/j.ress.2009.10.009",
language = "English",
volume = "95",
pages = "698--703",
journal = "Reliability Engineering and System Safety",
issn = "0951-8320",
publisher = "Elsevier",
number = "6",

}

RIS

TY - JOUR

T1 - Time series methods applied to failure prediction and detection

AU - Garcia, FP

AU - Pedregal, DJ

AU - Roberts, Clive

PY - 2010/6/1

Y1 - 2010/6/1

N2 - Point mechanisms are critical track elements on railway networks. A failure in a single point mechanism causes delays, increased railway operating costs and even fatal accidents. This paper describes the development of a new robust and automatic algorithm for failure detection of point mechanisms. Failures are detected by comparing what can be considered the 'expected' form of signals predicted from historical records of point mechanism operation with those actually measured. The expected shape is a forecast from a combination of a VARMA (vector auto-regressive moving-average) model and a harmonic regression model. The algorithm has been tested on a large dataset taken from an in-service point mechanism at Abbotswood Junction in the UK. The results show that the faults can be predicted and detected. (C) 2010 Elsevier Ltd. All rights reserved.

AB - Point mechanisms are critical track elements on railway networks. A failure in a single point mechanism causes delays, increased railway operating costs and even fatal accidents. This paper describes the development of a new robust and automatic algorithm for failure detection of point mechanisms. Failures are detected by comparing what can be considered the 'expected' form of signals predicted from historical records of point mechanism operation with those actually measured. The expected shape is a forecast from a combination of a VARMA (vector auto-regressive moving-average) model and a harmonic regression model. The algorithm has been tested on a large dataset taken from an in-service point mechanism at Abbotswood Junction in the UK. The results show that the faults can be predicted and detected. (C) 2010 Elsevier Ltd. All rights reserved.

KW - Safety

KW - Failure diagnostic

KW - Railway engineering

KW - Maintenance

U2 - 10.1016/j.ress.2009.10.009

DO - 10.1016/j.ress.2009.10.009

M3 - Article

VL - 95

SP - 698

EP - 703

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

SN - 0951-8320

IS - 6

ER -