2023
Galdo, Antía López; Guerrero-López, Alejandro; Olmos, Pablo M.; García, María Jesús Gómez
Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networks Artículo de revista
En: Engineering Applications of Artificial Intelligence, vol. 126, pp. 106840, 2023, ISSN: 0952-1976.
Resumen | Enlaces | BibTeX | Etiquetas: Condition monitoring, Convolutional Neural Networks, Crack detection, Deep learning, Railway axles, Vibration signal
@article{LOPEZGALDO2023106840,
title = {Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networks},
author = {Ant\'{i}a L\'{o}pez Galdo and Alejandro Guerrero-L\'{o}pez and Pablo M. Olmos and Mar\'{i}a Jes\'{u}s G\'{o}mez Garc\'{i}a},
url = {https://www.sciencedirect.com/science/article/pii/S0952197623010242},
doi = {https://doi.org/10.1016/j.engappai.2023.106840},
issn = {0952-1976},
year = {2023},
date = {2023-01-01},
journal = {Engineering Applications of Artificial Intelligence},
volume = {126},
pages = {106840},
abstract = {Maintaining railway axles is crucial to prevent catastrophic failures and enormous human and economic costs. In recent years, there has been a growing interest in the railway industry to adopt condition monitoring techniques to enhance the safety and efficiency of the rail transport system, which maintenance is currently based on periodic inspections. In this context, this work presents a technique for real-time crack diagnosis on railway axles, based on advanced 2D-Convolutional Neural Network (CNN) architectures applied to time\textendashfrequency representations of vibration signals. One of the critical novelties is introducing a differential CNN structure that captures the system’s statistical properties, enabling generalisation between different mechanical sets and conditions. The proposed system has been trained with data corresponding to a unique wheelset assembly, showing that the model is able to diagnose cracks on the three different wheelset tested in operation under 32 different combinations of conditions, such as load, speed, sense of rotation and vibration direction. Four different crack levels have been introduced, representing the maximum one a 0.08% of the axle diameter, and the method proposed achieved Area Under the Curve (AUC) score of 0.85, significantly outperforming results obtained with other architectures proposed in the state-of-the-art, the score of the next below is 0.76. The results demonstrate the effectiveness and practicality of this approach to accurately classify the four crack levels tested within a condition monitoring system for non-stationary conditions, that would enable reliable real-time diagnosis, thus paving the way towards a more robust and efficient railway axle maintenance system.},
keywords = {Condition monitoring, Convolutional Neural Networks, Crack detection, Deep learning, Railway axles, Vibration signal},
pubstate = {published},
tppubtype = {article}
}
Maintaining railway axles is crucial to prevent catastrophic failures and enormous human and economic costs. In recent years, there has been a growing interest in the railway industry to adopt condition monitoring techniques to enhance the safety and efficiency of the rail transport system, which maintenance is currently based on periodic inspections. In this context, this work presents a technique for real-time crack diagnosis on railway axles, based on advanced 2D-Convolutional Neural Network (CNN) architectures applied to time–frequency representations of vibration signals. One of the critical novelties is introducing a differential CNN structure that captures the system’s statistical properties, enabling generalisation between different mechanical sets and conditions. The proposed system has been trained with data corresponding to a unique wheelset assembly, showing that the model is able to diagnose cracks on the three different wheelset tested in operation under 32 different combinations of conditions, such as load, speed, sense of rotation and vibration direction. Four different crack levels have been introduced, representing the maximum one a 0.08% of the axle diameter, and the method proposed achieved Area Under the Curve (AUC) score of 0.85, significantly outperforming results obtained with other architectures proposed in the state-of-the-art, the score of the next below is 0.76. The results demonstrate the effectiveness and practicality of this approach to accurately classify the four crack levels tested within a condition monitoring system for non-stationary conditions, that would enable reliable real-time diagnosis, thus paving the way towards a more robust and efficient railway axle maintenance system.
2022
Pantoja-Rosero, B. G.; Oner, D.; Kozinski, M.; Achanta, R.; Fua, P.; Perez-Cruz, Fernando; Beyer, K.
TOPO-Loss for continuity-preserving crack detection using deep learning Artículo de revista
En: Construction and Building Materials, vol. 344, pp. 128264, 2022, ISSN: 0950-0618.
Resumen | Enlaces | BibTeX | Etiquetas: Crack detection, Deep learning, Masonry buildings, Post-earthquake assessment
@article{PANTOJAROSERO2022128264,
title = {TOPO-Loss for continuity-preserving crack detection using deep learning},
author = {B. G. Pantoja-Rosero and D. Oner and M. Kozinski and R. Achanta and P. Fua and Fernando Perez-Cruz and K. Beyer},
url = {https://www.sciencedirect.com/science/article/pii/S0950061822019250},
doi = {https://doi.org/10.1016/j.conbuildmat.2022.128264},
issn = {0950-0618},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Construction and Building Materials},
volume = {344},
pages = {128264},
abstract = {We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address these problems by adapting an approach previously proposed for reconstructing roads in aerial images, in which a Convolutional Neural Network is trained with a loss function specifically designed to encourage the continuity of thin structures and to accommodate imprecise annotations. We evaluate combinations of three loss functions (the Mean Squared Error, the Dice loss and the new connectivity-oriented loss) on two datasets using TernausNet, a deep network shown to attain state-of-the-art accuracy in crack detection. We herein show that combining these three losses significantly improves the topology of the predictions quantitatively and qualitatively. We also propose a new continuity metric, named Cracks Per Patch (CPP), and share a new dataset of images of earthquake-affected urban scenes accompanied by crack annotations. The dataset and implementations are publicly available for future studies and benchmarking (https://github.com/eesd-epfl/topo_crack_detection and https://doi.org/10.5281/zenodo.6769028).},
keywords = {Crack detection, Deep learning, Masonry buildings, Post-earthquake assessment},
pubstate = {published},
tppubtype = {article}
}
We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address these problems by adapting an approach previously proposed for reconstructing roads in aerial images, in which a Convolutional Neural Network is trained with a loss function specifically designed to encourage the continuity of thin structures and to accommodate imprecise annotations. We evaluate combinations of three loss functions (the Mean Squared Error, the Dice loss and the new connectivity-oriented loss) on two datasets using TernausNet, a deep network shown to attain state-of-the-art accuracy in crack detection. We herein show that combining these three losses significantly improves the topology of the predictions quantitatively and qualitatively. We also propose a new continuity metric, named Cracks Per Patch (CPP), and share a new dataset of images of earthquake-affected urban scenes accompanied by crack annotations. The dataset and implementations are publicly available for future studies and benchmarking (https://github.com/eesd-epfl/topo_crack_detection and https://doi.org/10.5281/zenodo.6769028).