OORNet: A deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains

Ye, Yunguang; Zhu, Bin; Huang, Ping; Peng, Bo

doi:10.1016/j.measurement.2022.111268

Cited by 80 publications

(33 citation statements)

References 46 publications

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“…The automatic features with deep learning approach have become an emerging research topic in PHM research [29]. Deep learning can solve the limitations of the expert experience by providing an efficient way to extract features automatically [30,31].…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

Chen¹,

Li²,

Huang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Railway turnout is one of the critical equipment of Switch & Crossing (S&C) Systems in railway, related to the train's safety and operation efficiency. With the advancement of intelligent sensors, data-driven fault detection technology for railway turnout has become an important research topic. However, little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout. This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios. First, the one-dimensional original time-series signal is converted into a twodimensional image by data pre-processing and 2D representation. Next, a binary classification model based on the convolutional autoencoder is developed to implement fault detection. The profile and structure information can be captured by processing data as images. The performance of our method is evaluated and tested on real-world operational current data in the metro stations. Experimental results show that the proposed method achieves better performance, especially in terms of error rate and specificity, and is robust in practical engineering applications.

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Section: Introductionmentioning

confidence: 99%

A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

Chen¹,

Li²,

Huang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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show abstract

“…A gear is a key mechanical component of the traction transmission system of railway vehicles, and its research focuses mainly on operational status assessment and health maintenance [24][25][26]. The current research mainly uses a dynamic modeling approach to study its fault mechanism [27][28][29][30], nonlinear behavior [31][32][33], and impact on vehicle dynamics [34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Characteristics and Fault Mechanism of the Gear Tooth Spalling in Railway Vehicles under Traction Conditions

Lin

Chen

et al. 2023

Applied Sciences

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Gear tooth spalling is one of the inevitable fault modes in the long-term service of the traction transmission system of railway vehicles, which can worsen the dynamic load of the rotating mechanical system and reduce the operating quality. Therefore, it is necessary to study its fault mechanism to guide fault diagnosis scientifically. This paper established a planar railway vehicle model with a traction transmission system and an analytical time-varying meshing stiffness (TVMS) model of the spalling spur gear. Then, it analyzed the dynamic characteristics under traction conditions. The research found that the spalling length and depth affect the amplitude of the TVMS at the defect, while the width affects the range of the TVMS loss. The crest factor is the best evaluation indicator in ideal low-noise environments due to its sensitivity and linearity, but it is not good in strong-noise environments. Similarly, a time–frequency analysis tool cannot significantly detect the sideband characteristics that are excited by spalling. After high-pass filtering, the root mean square and variance exhibit excellent classification and vehicle speed independence in strong-noise environments. This research achievement can provide adequate theoretical support for feature selection and making strategies for fault diagnosis of railway vehicle gear systems.

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“…The research above shows that the polygonal wheel can damage vehicle components, track and subgrade structure, threatening vehicle operation and line safety. Thus, naturally, fault analysis and diagnosis of the polygonal wheel have been extensive research [21,22]. Some phenomena only appear when the train is running at a variable speed [23].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Nonstationary Representations of a Railway Vehicle Considering Wheel Polygonalization under Variable Speed Conditions

Zhao

Yang

et al. 2023

Preprint

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Wheel Polygonalization is one of the most common failures of the wheelset, which can directly affect the safety and comfort of railway vehicle operation. In the actual condition, traction/braking torque continuously acts on the polygonal wheel fault can result in the modulation of its vibration representation, and then influence the effect of the feature exaction of the polygonal wheel faults. Considering this phenomenon, this study aims to clarify the forming mechanism of fault signals under variable speed conditions to improve the accuracy of feature extraction and ensure the safe operation of vehicles. This paper derived the Lagrangian equations with the dissipation function of a railway vehicle model with polygonal wheel fault under traction/braking conditions. After that, the modulation representation under constant speed, linear variable speeds, and nonlinear variable speeds are investigated, respectively. Finally, the vibration evolution of polygonal faults under different working conditions is visualized using statistical features. Compared with the constant speed, the curve in the time domain has more high-frequency fluctuations under variable conditions, and the phase modulation causes the half-wave asymmetry of the waveform. In the time-frequency domain, the continuous input of torque increases the harmonics frequency and side frequency response of fault response and excites the resonance frequency of the vehicle system. The dynamic evolution of fault statistical characteristics is positively correlated with speed and fault severity, and the fluctuation is sharper under braking conditions. The results can provide the theoretical support for feature extraction, interpretable features, and intelligent diagnosis of polygon faults.

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OORNet: A deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains

Cited by 80 publications

References 46 publications

A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

Dynamic Characteristics and Fault Mechanism of the Gear Tooth Spalling in Railway Vehicles under Traction Conditions

Dynamic Nonstationary Representations of a Railway Vehicle Considering Wheel Polygonalization under Variable Speed Conditions

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