Abstract
This paper focuses on the new model of classification of wagon bogie springs condition through images acquired by a wayside equipment. As such, we are discussing the application of a semi-supervised learning approach based on a deep rules-based (DRB) classifier learning approach to achieve a high classification of a bogie, and check if they either have spring problems or not. We use a pre-trained VGG19 deep convolutional neural network to extract the attributes from images to be used as input to the classifiers. The performance is calculated based on the data set composed of images provided by a Brazilian railway company which covers the two spring condition : normal condition (no elastic reserve problems) and bad condition (with elastic reserve problems). Also, an additive Gaussian noise level is applied to the images to challenge the proposed model. Finally, we discuss the performance analysis of the semi-supervised DRB (SSDRB) classifier and its distinctive characteristics compared with other classifiers. The reported results demonstrate a relevant performance of the SSDRB classifier applied to the questions raised.
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Acknowledgements
The authors acknowledge the MRS Logística S.A. for the essential support during this work. The authors also thank the financial support of CNPq (grant 433389/2018-4), FAPEMIG (APQ-02922-18).
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Neto, C.M.V., Honorio, L.G. & de Aguiar, E.P. Semi-supervised deep rule-based approach for the classification of Wagon Bogie springs condition. Evolving Systems 13, 653–666 (2022). https://doi.org/10.1007/s12530-022-09440-6
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DOI: https://doi.org/10.1007/s12530-022-09440-6