Convolutional Recurrent Neural Network for Fault Diagnosis of High‐Speed Train Bogie

Liang, Kaiwei; Qin, Na; Huang, Deqing; Fu, Yuanzhe

doi:10.1155/2018/4501952

Cited by 38 publications

(15 citation statements)

References 32 publications

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“…CNN is one of the widely utilized deep learning networks proposed by Lecun et al [54]. It is good at detecting tiny and meaningful spatial features and has the characteristics of sparse weights [55]. In general, CNN is applied to computer vision areas, such as image classification [56] and object detection [57].…”

Section: Cnn For Slab Arching Detectionmentioning

confidence: 99%

Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data

Gao

Zhong

et al. 2020

Applied Sciences

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During the long-term service of slab track, various external factors (such as complicated temperature) can result in a series of slab damages. Among them, slab arching changes the structural mechanical properties, deteriorates the track geometry conditions, and even threatens the operation of trains. Therefore, it is necessary to detect slab arching accurately to achieve effective maintenance. However, the current damage detection methods cannot satisfy high accuracy and low cost simultaneously, making it difficult to achieve large-scale and efficient arching detection. To this end, this paper proposed a vision-based arching detection method using track geometry data. The main works include: (1) data nonlinear deviation correction and arching characteristics analysis; (2) data conversion and augmentation; (3) design and experiments of convolutional neural network- based detection model. The results show that the proposed method can detect arching damages effectively, and the F1-score reaches 98.4%. By balancing the sample size of each pattern, the performance can be further improved. Moreover, the method outperforms the plain deep learning network. In practice, the proposed method can be employed to detect slab arching and help to make maintenance plans. The method can also be applied to the data-based detection of other structural damages and has broad prospects.

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Section: Cnn For Slab Arching Detectionmentioning

confidence: 99%

Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data

Gao

Zhong

et al. 2020

Applied Sciences

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“…In [ 43 ], Yang et al proposed an opening damper evaluation method, which used HHT to convert a 1-D signal into 3-D time-frequency images and used these images for training CNNs in order to evaluate the vibration of power transmission systems. In [ 59 ], Liang et al used the intrinsic mode function (IMF) components of EMD to construct the fault features, which were then used to train the CNN-based FD network, which was called CRNN. In [ 60 ], Chen et al proposed an EMD-based decomposing method, called adaptive sparsest narrow-band decomposition (ASNBD) and applied the ASNBD to FD in roller bearings.…”

Section: Related Workmentioning

confidence: 99%

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Gao

Wei

Zhou

et al. 2020

Sensors

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In this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (MEMS) inertial sensors using a bidirectional long short-term memory (BLSTM)-based Hilbert–Huang transform (HHT) and a convolutional neural network (CNN). First, the method for fault diagnosis of inertial sensors is formulated into an HHT-based deep learning problem. Second, we present a new BLSTM-based empirical mode decomposition (EMD) method for converting one-dimensional inertial data into two-dimensional Hilbert spectra. Finally, a CNN is used to perform fault classification tasks that use time–frequency HHT spectrums as input. According to our experimental results, significantly improved performance can be achieved, on average, for the proposed BLSTM-based EMD algorithm in terms of EMD computational efficiency compared with state-of-the-art algorithms. In addition, the proposed fault diagnosis method achieves high accuracy in fault classification.

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“…For time series prediction problems, the long-short-term memory (LSTM) neural network shows superior performance, and a LSTMbased bogie fault prediction method was studied in [25], where the spatial and temporal correlation of fault features can be learned from the original time series signals without any prior knowledge. In [26], a convolutional recurrent neural network (CRNN) was proposed for the HST bogie fault diagnosis, which can simultaneously achieve high accuracy and save time as it inherits the advantages of CNN and Simple Recurrent Unit.…”

Section: Bogie System Fault Diagnosismentioning

confidence: 99%

Advances in Fault Diagnosis for High-Speed Railway: A Review

Yin

et al. 2019

2019 6th International Conference on Frontiers of Industrial Engineering (ICFIE)

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The high speed railway (HSR) is a complex system with many subsystems and components. The reliability of its core subsystems is a key consideration in ensuring the safety and operation efficiency of the whole system. As the service time increases, the degradation of these subsystems and components may lead to a range of faults and deteriorate the whole system performance. To ensure the operation safety and to develop reasonable maintenance strategies, fault detection and isolation is an indispensable functionality in high speed railway systems. In this paper, the traction power supply system, bogie system, civil infrastructure system, and control and signaling system of HSR are briefly summarized, and then different fault diagnosis methods for these subsystems are comprehensively reviewed. Finally, some future research topics are discussed.Keywords-High-speed railway (HSR); fault diagnosis; traction power supply system (TPSS); bogie system I.

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Convolutional Recurrent Neural Network for Fault Diagnosis of High‐Speed Train Bogie

Cited by 38 publications

References 32 publications

Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data

Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Advances in Fault Diagnosis for High-Speed Railway: A Review

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