A New Model to Distinguish Railhead Defects Based on Set-Membership Type-2 Fuzzy Logic System
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A New Model to Distinguish Railhead Defects Based on Set-Membership Type-2 Fuzzy Logic System Eduardo P. de Aguiar1 • Thiago E. Fernandes1 Daniel D. Silveira3 • Marley M. B. R. Vellasco2
Fernando M. de A. Nogueira1 • Moise ´ s V. Ribeiro3
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Received: 4 May 2020 / Revised: 4 July 2020 / Accepted: 17 August 2020 Taiwan Fuzzy Systems Association 2020
Abstract This paper focuses on the new model for the classification of railhead defects, through images acquired by a rail inspection vehicle. In this regard, we discuss the use of set-membership concept, derived from the adaptive filter theory, into the training procedure of an upper and lower singleton type-2 fuzzy logic system, aiming to reduce computational complexity and to increase the convergence speed. The performance is based on the data set composed of images provided by a Brazilian railway company, which covers the four possible railhead defects (cracking, flaking, head-check and spalling) and the normal condition of the railhead. Additionally, we apply different levels of additive white Gaussian noise in the images in order to challenge the proposed model. Finally, we discuss performance analysis in terms of convergence speed, & Eduardo P. de Aguiar [email protected] Thiago E. Fernandes [email protected] Fernando M. de A. Nogueira [email protected] Daniel D. Silveira [email protected] Marley M. B. R. Vellasco [email protected] Moise´s V. Ribeiro [email protected] 1
Industrial and Mechanical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora/MG, Brazil
2
Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro/RJ, Brazil
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Electrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora/MG, Brazil
computational complexity reduction, and classification ratio. The reported results show that the proposal achieved improved convergence speed, slightly higher classification ratio and remarkable computation complexity reduction when we limit the number of epochs for training, which may be required under real-time constraint or low computational resource availability. Keywords Type-2 fuzzy logic systems Set-membership Computational complexity reduction Adaptive algorithms
1 Introduction Since the upcoming of steam-powered machines, rail transportation has become an effective solution for connecting urban centers, as well as a low-cost alternative to the industries’ transactions. Due to this circumstances, the rails are submitted to stressing cycles with heavy loads, which causes to increase their defects through material fatigue. Thus, image processing and computational intelligence techniques are increasingly participating in the solution of this scenario. Since their capacity to detect critical defects along the railway enables to increase the efficiency, safety, and environmental compatibility of this transportation systems. The type-1 fuzzy logic system (FLS) has been widely applied in cla
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