Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression

Hu, Zhongshuo; Yang, Jianwei; Yao, Dechen; Wang, Jinhai; Bai, Yongliang

doi:10.3390/e23060660

Cited by 9 publications

(9 citation statements)

References 47 publications

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“…In the existing electrified railroad operation lines, according to the statistics of various accidents in the past years, the vehicle equipment failures caused by pantographs account for a relatively large proportion. Pantograph is an important part of the vehicle but also is in direct contact with the contact network part, in the vehicle high-speed travel by wear and tear, and has a high probability of failure [4][5][6]. Rail vehicle power supply and operation disruptions have a significant impact on line transportation order and are a major issue for rail cars.…”

Section: Introductionmentioning

confidence: 99%

Design of Faster R-CNN-Based Fault Detection Method for Subway Vehicles

Yao

2022

Computational and Mathematical Methods in Medicine

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A substantial amount of maintenance and fault data is not properly utilized in the daily maintenance of pantographs in urban metro cars. Pantograph fault analysis can begin with three factors: the external environment, internal flaws, and joint behavior. Based on the analysis of pantograph fault types, corresponding measures are proposed in terms of pantograph fault handling and maintenance strategies, in order to provide safety guarantee for the safe and effective realization of rail transit vehicle speed-up and also provide reference for the maintenance and overhaul of pantographs. For the problem of planned maintenance no longer meeting current pantograph maintenance requirements, a defect diagnosis system based on a combination of faster R-CNN neural networks is presented. The pantograph image features are extracted by introducing an alternative to the original feature extraction module that can extract deep-level image features and achieve feature reuse, and the data transformation operations such as image rotation and enhancement are used to expand the sample set in the experiment to enhance the detection effect. The simulation results demonstrate that the diagnosis procedure is quick and accurate.

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Section: Introductionmentioning

confidence: 99%

Design of Faster R-CNN-Based Fault Detection Method for Subway Vehicles

Yao

2022

Computational and Mathematical Methods in Medicine

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“…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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“…Ma et al [15] proposed a method to extract the main components from time-frequency images, which realized the bearing fault diagnosis under variable working conditions. Hu et al [16] proposed an adaptive timedomain signal segmentation method, which divides long-term non-steady-state signals into multiple short-term steady-state signals, which can effectively suppress the high-amplitude components of the vibration signal of subway gearboxes. Xu et al [17] proposed generalized S-simultaneous extrusion to extract variation, a new time-frequency post-processing algorithm with good noise robustness.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of bearings in multiple working conditions based on adaptive time-varying parameters short-time Fourier synchronous squeeze transform

wei

Yang

Yao

et al. 2022

Meas. Sci. Technol.

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Rolling bearings are commonly used components in rotating machinery and play a vital role. When the bearing fails, if it cannot be found and repaired in time, it will cause great economic losses. Time-frequency analysis has been widely used for bearing fault signals under non-stationary operating conditions, but the existing methods have problems such as poor adaptability under multiple operating conditions. At the same time, the low time-frequency resolution and poor energy aggregation also affect the fault feature extraction effect. Aiming at these problems, this paper proposes a bearing fault detection method AFSST, which combines empirical mode decomposition and adaptive time-varying parameter short-time Fourier synchronous squeezing transform, it solves the problem of adapting to signals under multiple operating conditions; A weighted least squares estimation time-varying parameter algorithm is proposed, which improves the calculation speed by 29% under the premise of ensuring the calculation accuracy; A time-varying index of Energy Effective Compression Ratio (Eecr) is proposed to accurately measure the time-varying energy aggregation of time-frequency analysis methods. Using short-time Fourier transform (STFT), continuous wavelet transform (CWT), wavelet synchrosqueezed transform (WSST) and AFSST to analyze the simulated FM signal, the results show that the AFSST transform has better time-frequency resolution and higher energy-efficient compression rate globally. Through the verification of the fault experimental data of rolling bearings, the diagnosis method proposed in this paper can accurately extract the bearing fault characteristics, has a good diagnosis ability in the multi-working operating environment, and has strong robustness and anti-noise interference.

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Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression

Cited by 9 publications

References 47 publications

Design of Faster R-CNN-Based Fault Detection Method for Subway Vehicles

Design of Faster R-CNN-Based Fault Detection Method for Subway Vehicles

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

Fault diagnosis of bearings in multiple working conditions based on adaptive time-varying parameters short-time Fourier synchronous squeeze transform

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