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

Wu, Yunpu; Jin, Weidong; Ren, Junxiao; Sun, Zhanquan

doi:10.1155/2019/7230194

Cited by 10 publications

(7 citation statements)

References 44 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ren et al [29] considered detection of lateral and yaw damper failures by means of a novel 1D-ConvLSTM time distributed convolutional neural network (CLTD-CNN). Wu et al [38] proposed synchrony group convolutions to construct a fault diagnosis scheme for we propose synchrony group convolutions for a high-speed train bogie. The examined five categories of faults: air spring fault, wheel-box spring fault, lateral damper fault, yaw damper fault, and vertical damper fault.…”

Section: Rail Vehicles' Suspension Fault Detection Methods -State Of ...mentioning

confidence: 99%

Feasibility study of a rail vehicle damper fault detection by artificial neural Indexed by: networks

Melnik¹,

Koziak²,

Dižo³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

The aim of the study was to investigate rail vehicle dynamics under primary suspension dampers faults and explore possibility of its detection by means of artificial neural networks. For these purposes two types of analysis were carried out: preliminary analysis of 1 DOF rail vehicle model and a second one - a passenger coach benchmark model was tested in multibody simulation software - MSC.Adams with use of VI-Rail package. Acceleration signals obtained from the latter analysis served as an input data into the artificial neural network (ANN). ANNs of different number of hidden layers were capable of detecting faults for the trained suspension fault cases, however, achieved accuracy was below 63% at the best. These results can be considered satisfactory considering the complexity of dynamic phenomena occurring in the vibration system of a rail vehicle.

show abstract

Section: Rail Vehicles' Suspension Fault Detection Methods -State Of ...mentioning

confidence: 99%

Feasibility study of a rail vehicle damper fault detection by artificial neural Indexed by: networks

Melnik¹,

Koziak²,

Dižo³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

show abstract

“…In the fault classification step, two classifiers were compared, i.e., artificial neural networks (ANN), and k-nearest neighbor (k-NN), and the k-NN classifier were proved to be more reliable than the ANN classifier. (4) DL-based [43] In recent years, DL methods have begun to be applied to various industries, but research on RVSFD is relatively scarce. In [43], to diagnose the faults of suspension systems of high-speed trains, three synchrony measurements (instantaneous phase synchrony, amplitude envelope synchrony, and composite synchrony) were applied to estimate the similarity between bogie acceleration signals, and a synchrony group convolutional network was proposed for feature extraction and pattern classification of the multichannel monitoring system.…”

Section: Data-driven Approachmentioning

confidence: 99%

“…(4) DL-based [43] In recent years, DL methods have begun to be applied to various industries, but research on RVSFD is relatively scarce. In [43], to diagnose the faults of suspension systems of high-speed trains, three synchrony measurements (instantaneous phase synchrony, amplitude envelope synchrony, and composite synchrony) were applied to estimate the similarity between bogie acceleration signals, and a synchrony group convolutional network was proposed for feature extraction and pattern classification of the multichannel monitoring system. The effectiveness of the method was validated by a simulation dataset.…”

Section: Data-driven Approachmentioning

confidence: 99%

Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear

Huang

Zhang

2021

Rail. Eng. Science

View full text Add to dashboard Cite

Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety. Firstly, the existing methods concerning fault detection or isolation of train suspension systems are briefly reviewed and divided into two categories, i.e., model-based and data-driven approaches. The advantages and disadvantages of these two categories of approaches are briefly summarized. Secondly, a 1D convolution network-based fault diagnostic method for high-speed train suspension systems is designed. To improve the robustness of the method, a Gaussian white noise strategy (GWN-strategy) for immunity to track irregularities and an edge sample training strategy (EST-strategy) for immunity to wheel wear are proposed. The whole network is called GWN-EST-1DCNN method. Thirdly, to show the performance of this method, a multibody dynamics simulation model of a high-speed train is built to generate the lateral acceleration of a bogie frame corresponding to different track irregularities, wheel profiles, and secondary suspension faults. The simulated signals are then inputted into the diagnostic network, and the results show the correctness and superiority of the GWN-EST-1DCNN method. Finally, the 1DCNN method is further validated using tracking data of a CRH3 train running on a high-speed railway line.

show abstract

“…The goal is improved business shareholder value by productivity improvement and/or cost reduction. More specifically, in railway maintenance applications, machine learning has been researched for bogie maintenance [8], track defect detection [9] and for predicting wheel and rail interface wear [10].…”

Section: Introductionmentioning

confidence: 99%

Machine learning: approaches to predicting reliability and developing maintenance strategies

Singh

2023

Materials Research Proceedings

View full text Add to dashboard Cite

Abstract. Current approaches to maintenance of rolling stock bogies are focused on compliance to wear limits as stipulated by OEM specifications. OEM recommendations are critical to providing an industry wide approach to safety and compliance. These are not operation specific and are often not the most cost-effective solutions. A system approach to reliability is an established approach that is applied in less complex systems where the relationships between components are well defined with historical data and predictable conditions. Extending this approach to more complex multi-variate systems where many relationships are not intuitively obvious or mathematically defined presents a challenge. Machine learning techniques have been applied to address such problems with examples in image recognition, tool wear prediction using multiple sensory inputs and estimating railway bogie wear using vibration inputs. [8,9,10] The aim of the study is to extend and adapt machine-learning techniques to the area of developing maintenance strategies for optimal business benefit with a specific focus on railway bogie maintenance. This study aims to present an insight into the variables, which includes bogie tracking condition affecting track side wear rate. A finding is that an in-depth study of each independent variable’s individual impact is a necessary step to efficient modelling. These include track geometry, operating and bogie component wear variables. Track side wear, curve radius, superelevation and track top variance have been found to be significant predictors of track side wear rate. These impact predictions are not consistent between the different rail tracks and are not exhaustive. Specifically, the impact of bogie performance requires inclusion. Combining these variables mathematically using statistical inference and convolutional theory with maximum likelihood estimators would establish a predictor for side wear rate for the specific operation. The paper finally discusses the relationship of area wear rate to side wear rate and the influences of grinding frequency and rail material type.

show abstract

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

Cited by 10 publications

References 44 publications

Feasibility study of a rail vehicle damper fault detection by artificial neural Indexed by: networks

Feasibility study of a rail vehicle damper fault detection by artificial neural Indexed by: networks

Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear

Machine learning: approaches to predicting reliability and developing maintenance strategies

Contact Info

Product

Resources

About