Panhead accelerations-based methodology for monitoring the stagger in overhead contact line systems

TY - JOUR

T1 - Panhead accelerations-based methodology for monitoring the stagger in overhead contact line systems

AU - Blanco, Blas

AU - Errandonea, Itxaro

AU - Beltrán, Sergio

AU - Arrizabalaga, Saioa

AU - Alvarado, Unai

N1 - Publisher Copyright: © 2022 The Author(s)

PY - 2022/5

Y1 - 2022/5

N2 - The monitoring of overhead contact lines (OCL) is a key part of railway infrastructure maintenance. This paper proposes a methodology to assess the lateral geometry of contact wire, the so-called stagger, by using the dynamic response of a pantograph. The methodology is tested in a validated virtual environment that resembles the behaviour of the pantograph when it interacts with the OCL. A signal processing is developed to define features relating the lateral position of the contact wire with the vertical acceleration of the contact strip. It is demonstrated that these features have a clear and close connection with the lateral position of the contact wire. Subsequently, model-driven machine learning algorithms are defined using these features to address the OCL stagger prediction and the detection of out-of-range lateral displacement due to a faulty steady-arm. The methodology shows a good prediction performance in the estimation of the stagger amplitude/central position and the steady-arms diagnosis. The prediction of the stagger amplitude is performed with a root-mean-square error of 4.7(10)mm. In addition, the area under the Precision–Recall curve is 0.952 CI95 [0.940, 0.962] for the steady-arms diagnosis.

AB - The monitoring of overhead contact lines (OCL) is a key part of railway infrastructure maintenance. This paper proposes a methodology to assess the lateral geometry of contact wire, the so-called stagger, by using the dynamic response of a pantograph. The methodology is tested in a validated virtual environment that resembles the behaviour of the pantograph when it interacts with the OCL. A signal processing is developed to define features relating the lateral position of the contact wire with the vertical acceleration of the contact strip. It is demonstrated that these features have a clear and close connection with the lateral position of the contact wire. Subsequently, model-driven machine learning algorithms are defined using these features to address the OCL stagger prediction and the detection of out-of-range lateral displacement due to a faulty steady-arm. The methodology shows a good prediction performance in the estimation of the stagger amplitude/central position and the steady-arms diagnosis. The prediction of the stagger amplitude is performed with a root-mean-square error of 4.7(10)mm. In addition, the area under the Precision–Recall curve is 0.952 CI95 [0.940, 0.962] for the steady-arms diagnosis.

KW - Infrastructure monitoring

KW - Machine learning

KW - Pantograph–catenary interaction

KW - Railway

KW - Stagger

KW - Virtual environment

UR - http://www.scopus.com/inward/record.url?scp=85124228255&partnerID=8YFLogxK

U2 - 10.1016/j.mechmachtheory.2022.104742

DO - 10.1016/j.mechmachtheory.2022.104742

M3 - Journal article

AN - SCOPUS:85124228255

SN - 0094-114X

VL - 171

JO - Mechanism and Machine Theory

JF - Mechanism and Machine Theory

M1 - 104742

ER -