Rail Defect Detection Method Based on Recurrent Neural Network

Xu, Qinhua; Zhao, Qinjun; Yu, Gang; Wang, Liguo; Shen, Tao

doi:10.23919/ccc50068.2020.9188823

Cited by 14 publications

(10 citation statements)

References 9 publications

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“…[24,[32][33][34][35] use convolutional neural networks (CNN), Refs. [36][37][38] use LSTM, and [39] uses a combination of both CNN and RNN called convolutional-recurrent neural network (CRNN). In transfer learning approaches, Refs.…”

Section: Related Workmentioning

confidence: 99%

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Railway Track Inspection Using Deep Learning Based on Audio to Spectrogram Conversion: An on-the-Fly Approach

Hashmi

Ibrahim

Bajwa

et al. 2022

Sensors

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The periodic inspection of railroad tracks is very important to find structural and geometrical problems that lead to railway accidents. Currently, in Pakistan, rail tracks are inspected by an acoustic-based manual system that requires a railway engineer as a domain expert to differentiate between different rail tracks’ faults, which is cumbersome, laborious, and error-prone. This study proposes the use of traditional acoustic-based systems with deep learning models to increase performance and reduce train accidents. Two convolutional neural networks (CNN) models, convolutional 1D and convolutional 2D, and one recurrent neural network (RNN) model, a long short-term memory (LSTM) model, are used in this regard. Initially, three types of faults are considered, including superelevation, wheel burnt, and normal tracks. Contrary to traditional acoustic-based systems where the spectrogram dataset is generated before the model training, the proposed approach uses on-the-fly feature extraction by generating spectrograms as a deep learning model’s layer. Different lengths of audio samples are used to analyze their performance with each model. Each audio sample of 17 s is split into 3 variations of 1.7, 3.4, and 8.5 s, and all 3 deep learning models are trained and tested against each split time. Various combinations of audio data augmentation are analyzed extensively to investigate models’ performance. The results suggest that the LSTM with 8.5 split time gives the best results with the accuracy of 99.7%, the precision of 99.5%, recall of 99.5%, and F1 score of 99.5%.

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Section: Related Workmentioning

confidence: 99%

“…The study used CNN for the binary classification of rail tracks into 'healthy' or 'faulty' tracks. The study [36] used an ultrasonic vehicle for finding flaws in the railway track. The LSTM model is used for the detection which shows its feasibility of detection faults at the speed of 15 km/h.…”

Section: Related Workmentioning

confidence: 99%

Railway Track Inspection Using Deep Learning Based on Audio to Spectrogram Conversion: An on-the-Fly Approach

Hashmi

Ibrahim

Bajwa

et al. 2022

Sensors

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“…Despite the images were manually labelled into six defect classes, the paper focuses on a binary classification by grouping the samples into defective and non-defective images. RNN has been used in [38], where the inspection was performed using an ultrasonic flaw detection vehicle collecting B-scan data of ten different types of defects. The idea was to turn the problem into identification and classification of "sequence languages".…”

Section: B Railsmentioning

confidence: 99%

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

et al. 2022

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Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions.

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“…Since deep learning methods have been applied to computer vision tasks, researchers have widely applied deep learning methods such as convolutional neural network (CNN) [3], Deep Belief Network (DBN) [4], Recurrent Neural Network (RNN) [5], Autoencoder (AE) [6] and Generative Adversarial Network (GAN) [7] to various defect detection tasks and achieved good performance. With the advent of the information age, data volume and complexity show an exponential growth trend.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

Chang²,

Zhang³

et al. 2022

IEEE Access

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Rail Defect Detection Method Based on Recurrent Neural Network

Cited by 14 publications

References 9 publications

Railway Track Inspection Using Deep Learning Based on Audio to Spectrogram Conversion: An on-the-Fly Approach

Railway Track Inspection Using Deep Learning Based on Audio to Spectrogram Conversion: An on-the-Fly Approach

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

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