Data-driven Detection and Diagnosis of Incipient Faults in Electrical Drives of High-Speed Trains

Chen, Hongtian; Jiang, Bin; Chen, Wen; Yi, Hui

doi:10.1109/tie.2018.2863191

Cited by 138 publications

(50 citation statements)

References 33 publications

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“…This approach was validated to learn nonlinear state-feedback control for linear output reference model tracking, which is innovative and can be applied to the train control system. Besides, data-driven methods also have been used for fault detection and diagnosis in the area of rail transit [35]. These inspiring studies give us a new angle to view the problem of building the train dynamic model.…”

Section: Related Workmentioning

confidence: 99%

Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction

Tang

Gao

2019

Algorithms

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The automatic train operation system is a significant component of the intelligent railway transportation. As a fundamental problem, the construction of the train dynamic model has been extensively researched using parametric approaches. The parametric based models may have poor performances due to unrealistic assumptions and changeable environments. In this paper, a long short-term memory network is carefully developed to build the train dynamic model in a nonparametric way. By optimizing the hyperparameters of the proposed model, more accurate outputs can be obtained with the same inputs of the parametric approaches. The proposed model was compared with two parametric methods using actual data. Experimental results suggest that the model performance is better than those of traditional models due to the strong learning ability. By exploring a detailed feature engineering process, the proposed long short-term memory network based algorithm was extended to predict train speed for multiple steps ahead.

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Section: Related Workmentioning

confidence: 99%

Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction

Tang

Gao

2019

Algorithms

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“…Furthermore, the fault diagnosis approaches implemented in Railway traction drives are summarized in Table 2. [72,73] Speed and current PCA, ICA [74,75] Voltage, current and speed Model-based estimator [32,76] Power switches Wavelet entropy [77] Power switches Wavelet energy and SVM [78] Power switches Current FFT [79] Power switches Fault signatures analysis [80] Power switches Least squares [81] Capacitor…”

Section: Fault Diagnosis In Railway Traction Drivesmentioning

confidence: 99%

“…Furthermore, this approach does not require the probability density function (PDF) calculation, reducing the computational cost and allowing an online implementation. Recently, a new approach based on PCA and called probability relevant PCA [75], presents a comparison among different PCA-based approaches. It reduces the missing alarm ratio and false alarm rate in case of incipient faults in current and voltage sensors of an electrical drive for a high-speed train.…”

Section: Fault Diagnosis In Sensorsmentioning

confidence: 99%

A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives

et al. 2018

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During the last decade, due to the increasing importance of reliability and availability, railway industry is making greater use of fault diagnosis approaches for early fault detection, as well as Condition-based maintenance frameworks. Due to the influence of traction drive in the railway system availability, several research works have been focused on Fault Diagnosis for Railway traction drives. Fault diagnosis approaches have been applied to electric machines, sensors and power electronics. Furthermore, Condition-based maintenance framework seems to reduce corrective and Time-based maintenance works in Railway Systems. However, there is not any publication that summarizes all the research works carried out in Fault diagnosis and Condition-based Maintenance frameworks for Railway Traction Drives. Thus, this review presents the development of Health Assessment and Fault Diagnosis in Railway Traction Drives during the last decade.

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“…Gasparetto et al [20] proposed a data-driven approach for detecting the incipient instability of the bogie, which is based on random decrement technique and Prony method. Chen et al [21] proposed a probability-relevant PCA method for detecting incipient sensor faults in electrical drive systems of highspeed trains. Zhuang et al [22] developed a combined method based on convolution sparse representation, Hilbert transform, and manifold learning for detecting bearing faults of trains.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of High-Speed Train Bogie Based on Synchrony Group Convolutions

Jin

Ren

et al. 2019

Shock and Vibration

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Health monitoring and fault diagnosis of a high-speed train is an important research area in guaranteeing the safe and long-term operation of the high-speed railway. For a multichannel health monitoring system, a major technical challenge is to extract information from different channels with divergence patterns as a result of distinct types and layout of sensors. To this end, this paper proposes a novel group convolutional network based on synchrony information. The proposed method is able to gather signals with similar patterns and process these channels with specific groups of neurons while simultaneously assigning signals with significant difference to different groups. In this approach, the feature can be extracted more effectively and the performance can be improved, owing to the sharing of filters for similar patterns. The effectiveness of the method is validated on high-speed train fault dataset. Experiments show that the proposed model performs better than normal convolutions and normal group convolutions on this task, which achieves an accuracy of 98.27% (σ = 1.73) with good computational efficiency.

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Data-driven Detection and Diagnosis of Incipient Faults in Electrical Drives of High-Speed Trains

Cited by 138 publications

References 33 publications

Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction

Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction

A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives

Fault Diagnosis of High-Speed Train Bogie Based on Synchrony Group Convolutions

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