Transmissibility-based methods for damage identification on a railway bogie frame

Abstract The bogie frame is a safety–critical component of modern railway vehicles. Consequently, railway companies periodically inspect the bogie frame to guarantee a timely damage detection. Current inspection strategies could benefit from condition monitoring during the operation using adequate sensor systems and algorithms. However, the application of methods for damage detection using the bogie frame response is still limited. This work aims to identify the sensitivity of two complementary methods based on the transmissibilities—an updated form of the transmissibility damage indicator (TDI) method and the maximum occurrences (MO) method—to detect and locate structural damage. This analysis is supported by flexible multibody simulations that provide the nominal and abnormal response of a locomotive in realistic operational conditions. This work first assesses the effect of differences in track inputs on the sensitivity of the TDI method. Both the length of acceleration signals and the number and location of sensors are analyzed, from the perspective of the damage detection capacity. The results show that the TDI method successfully detects cracks with a moderate-to-large dimension, while the MO method identifies the region where those cracks occur. The transmissibility-based methods are a reliable technique that may contribute to enhance the inspection of railway vehicles.

Authors

Institutions

Publication Details

Journal
Journal of the Brazilian Society of Mechanical Sciences and Engineering
Published
2025-02-13
DOI
https://doi.org/10.1007/s40430-025-05419-x
Citations
3
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
2.86

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Transmissibility-based methods for damage identification on a railway bogie frame

Jorge Ambrósio, Pedro Millan, J. Pagaimo, N. M. M. Maia et al.
3 citations
Journal of the Brazilian Society of Mechanical Sciences and Engineering
Structural Health Monitoring Techniques
2.86
article

Transmissibility-based methods for damage identification on a railway bogie frame

Jorge Ambrósio, Pedro Millan, J. Pagaimo, N. M. M. Maia, J. N. Costa
article en
3 citations

Abstract

Abstract The bogie frame is a safety–critical component of modern railway vehicles. Consequently, railway companies periodically inspect the bogie frame to guarantee a timely damage detection. Current inspection strategies could benefit from condition monitoring during the operation using adequate sensor systems and algorithms. However, the application of methods for damage detection using the bogie frame response is still limited. This work aims to identify the sensitivity of two complementary methods based on the transmissibilities—an updated form of the transmissibility damage indicator (TDI) method and the maximum occurrences (MO) method—to detect and locate structural damage. This analysis is supported by flexible multibody simulations that provide the nominal and abnormal response of a locomotive in realistic operational conditions. This work first assesses the effect of differences in track inputs on the sensitivity of the TDI method. Both the length of acceleration signals and the number and location of sensors are analyzed, from the perspective of the damage detection capacity. The results show that the TDI method successfully detects cracks with a moderate-to-large dimension, while the MO method identifies the region where those cracks occur. The transmissibility-based methods are a reliable technique that may contribute to enhance the inspection of railway vehicles.

Journal of the Brazilian Society of Mechanical Sciences and EngineeringVol. 47(3)
University of Lisbon (PT)
Universidade de Lisboa, Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 12%
Structural Health Monitoring Techniques
2.86
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.