Railway track breakage detection method using vibration estimating sensor network: A novel approach

Sharma, Kalpana; Maheshwari, Saurabh; Solanki, Ruchika; Khanna, Vivek

doi:10.1109/icacci.2014.6968518

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…Several sensing systems have been developed and installed for more detailed examinations of trains' components. Sharma et al [7] detected breakage of railway tracks using vibration sensors. Sireesha et al [8] used a radio frequency-based method to detect rails' broken status.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Generative Adversarial Network-based Missing Data Handling and Remaining Useful Life Estimation for Smart Train Control and Monitoring Systems

Lee

Han

Park

2020

Journal of Advanced Transportation

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As railway is considered one of the most significant transports, sudden malfunction of train components or delayed maintenance may considerably disrupt societal activities. To prevent this issue, various railway maintenance frameworks, from “periodic time-based and distance-based traditional maintenance frameworks” to “monitoring/conditional-based maintenance systems,” have been proposed and developed. However, these maintenance frameworks depend on the current status and situations of trains and cars. To overcome these issues, several predictive frameworks have been proposed. This study proposes a new and effective remaining useful life (RUL) estimation framework using big data from a train control and monitoring system (TCMS). TCMS data is classified into two types: operation data and alarm data. Alarm or RUL information is extracted from the alarm data. Subsequently, a deep learning model achieves the mapping relationship between operation data and the extracted RUL. However, a number of TCMS data have missing values due to malfunction of embedded sensors and/or low life of monitoring modules. This issue is addressed in the proposed generative adversarial network (GAN) framework. Both deep neural network (DNN) models for a generator and a predictor estimate missing values and predict train fault, simultaneously. To prove the effectiveness of the proposed GAN-based predictive maintenance framework, TCMS data-based case studies and comparisons with other methods were carried out.

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Section: Background and Literature Reviewmentioning

confidence: 99%