2019 4th International Conference on Intelligent Transportation Engineering (ICITE) 2019
DOI: 10.1109/icite.2019.8880246
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Dynamic Modelling and Acceleration Signal Analysis of Rail Surface Defects for Enhanced Rail Condition Monitoring and Diagnosis

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Cited by 19 publications
(5 citation statements)
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“…Usually, mechanical detection is performed by manual work and vision, but such detection is time-consuming and subjective and offers low accuracy [5]. Automated detection methods are also used, such as ultrasonic detection [6,7], eddy current detection [8,9], magnetic flux leakage detection [10,11], and so on [12][13][14][15][16][17][18], but these methods are easily influenced by the hardware of the equipment. The detection methods of deep learning [19][20][21][22][23] focus on image features, and compared to the abovementioned methods, the methods of deep learning are quicker and more accurate with regard to detecting rail defects.…”
Section: Introductionmentioning
confidence: 99%
“…Usually, mechanical detection is performed by manual work and vision, but such detection is time-consuming and subjective and offers low accuracy [5]. Automated detection methods are also used, such as ultrasonic detection [6,7], eddy current detection [8,9], magnetic flux leakage detection [10,11], and so on [12][13][14][15][16][17][18], but these methods are easily influenced by the hardware of the equipment. The detection methods of deep learning [19][20][21][22][23] focus on image features, and compared to the abovementioned methods, the methods of deep learning are quicker and more accurate with regard to detecting rail defects.…”
Section: Introductionmentioning
confidence: 99%
“…Balouchi et al [7] also reported that the dynamic responses in terms of axle box accelerations, under damage conditions, can be considered as the most reliable evidence of the extent of the damage to the track. Chang et al [33] applied a continuous wavelet transform (CWT) to detect the resonant frequency of the car body, using vertical and lateral acceleration registered at the floor level, under rail [34][35][36] and wheel wear damage conditions. Erduran et al [37] developed a methodology based on CWT for the detection of bridge vibration frequencies under track damage, using simulated bogie vibration signals, and found that the developed approach can detect the bridge vibration frequency with acceptable accuracy.…”
Section: Introductionmentioning
confidence: 99%
“…Dois métodos de aprendizado de máquina foram introduzidos para detecção de defeitos em rodas de trens (Krummenacher et al, 2017). Outro trabalho (Ng et al, 2019) apresentou um sistema para verificar as relações entre os defeitos da superfície do trilho (RSDs, do inglês Rail Surface Defects) e seus correspondentes sinais de aceleração da caixa de eixo (ABA, do inglês Axle Box Acceleration). Além disso, foi investigada a influência da distância entre os dormentes no crescimento da corrugação ferroviária (Ng et al, 2018).…”
Section: Introdu ç ãOunclassified