B-spline approach for failure detection and diagnosis on railway point mechanisms case study
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B-spline approach for failure detection and diagnosis on railway point mechanisms case study. / García Márquez, Fausto Pedro; Chacón Muñoz, Jesús Miguel; Tobias, Andrew Mark.
In: Quality Engineering, Vol. 27, No. 2, 03.04.2015, p. 177-185.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - B-spline approach for failure detection and diagnosis on railway point mechanisms case study
AU - García Márquez, Fausto Pedro
AU - Chacón Muñoz, Jesús Miguel
AU - Tobias, Andrew Mark
PY - 2015/4/3
Y1 - 2015/4/3
N2 - In railway transportation the safety and reliability in operations of railway point mechanism must be ensured in order to improve the quality of the service. This article presents a case study in a railway turnout (U.S.: switch). The case study reports how the effect of the operating force data sampled and monitored during the switching of a railway point mechanism can be converted into continuous polynomial B-spline functions. These functions are employed to define, and periodically to update, tolerance bands for the purpose of condition monitoring. Data from variously faulty mechanisms were converted similarly, and the profiles found to differ sufficiently not only to detect 100 percent of faults but, from the distinctive shapes of the profiles, to diagnose (i.e., distinguish correctly between) some 70-80 percent of them.
AB - In railway transportation the safety and reliability in operations of railway point mechanism must be ensured in order to improve the quality of the service. This article presents a case study in a railway turnout (U.S.: switch). The case study reports how the effect of the operating force data sampled and monitored during the switching of a railway point mechanism can be converted into continuous polynomial B-spline functions. These functions are employed to define, and periodically to update, tolerance bands for the purpose of condition monitoring. Data from variously faulty mechanisms were converted similarly, and the profiles found to differ sufficiently not only to detect 100 percent of faults but, from the distinctive shapes of the profiles, to diagnose (i.e., distinguish correctly between) some 70-80 percent of them.
KW - B-spline
KW - failure detection and diagnosis
KW - point mechanisms
KW - remote condition monitoring
UR - http://www.scopus.com/inward/record.url?scp=84928563957&partnerID=8YFLogxK
U2 - 10.1080/08982112.2014.933980
DO - 10.1080/08982112.2014.933980
M3 - Article
AN - SCOPUS:84928563957
VL - 27
SP - 177
EP - 185
JO - Quality Engineering
JF - Quality Engineering
SN - 0898-2112
IS - 2
ER -