Predicting the remaining useful life of rails based on improved deep spiking residual neural network

He, Jing; Xiao, Zunguang; Zhang, Changfan

doi:10.1016/j.psep.2024.06.008

Process Safety and Environmental Protection

2024

DOI: 10.1016/j.psep.2024.06.008

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Predicting the remaining useful life of rails based on improved deep spiking residual neural network

Jing He,

Zunguang Xiao,

Changfan Zhang

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2024

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Cited by 2 publications

References 33 publications

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Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning

Zhang,

Wang,

2024

BDCC

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The suspension parameters of heavy-duty freight trains can deviate from their initial design values due to material aging and performance degradation. While traditional multibody dynamics simulation models are usually designed for fixed working conditions, it is difficult for them to adequately analyze the safety status of the vehicle–line system in actual operation. To address this issue, this research provides a suspension parameter estimation technique based on CNN-GRU. Firstly, a prototype C80 train was utilized to build a simulation model for multibody dynamics. Secondly, six key suspension parameters for wheel–rail force were selected using the Sobol global sensitivity analysis method. Then, a CNN-GRU proxy model was constructed, with the actually measured wheel–rail forces as a reference. By combining this approach with NSGA-II (Non-dominated Sorting Genetic Algorithm II), the key suspension parameters were calculated. Finally, the estimated parameter values were applied into the vehicle–line coupled multibody dynamical model and validated. The results show that, with the corrected dynamical model, the relative errors of the simulated wheel–rail force are reduced from 9.28%, 6.24% and 18.11% to 7%, 4.52% and 10.44%, corresponding to straight, curve, and long and steep uphill conditions, respectively. The wheel–rail force simulation’s precision is increased, indicating that the proposed method is effective in estimating the suspension parameters for heavy-duty freight trains.

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Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning

Zhang,

Wang,

2024

BDCC

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A Performance Evaluation Method for Long and Steep Uphill Sections of Heavy-Haul Railway Lines

He,

Di,

Zhang

et al. 2024

Safety

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Any system for evaluating the safety service performance of heavy-haul railway lines must effectively reflect the real-time service status of the line. The working conditions of heavy-load lines are complex and diverse, particularly on uphill sections. Existing evaluation systems struggle to accurately reflect the service conditions of long and steep uphill sections bearing heavy loads, posing a significant threat to the safe operation of these lines. To address this problem, we propose a new method for evaluating the safety service performance of long and steep uphill sections of heavy-haul railway lines by establishing a scoring system based on the Analytic Hierarchy Process (AHP). First, damage indicators for heavy-haul lines are categorized into three groups: track geometry status indicators, track structure status indicators, and track traffic status indicators. Using data from existing heavy-haul lines and maintenance experiences, we determine a score deduction standard, classifying lines into four levels based on their safety service quality. Next, we establish a coefficient table for the service performance of long and steep uphill sections after the corresponding scores are deducted. Using data for the length and elevation grade of the actual uphill section, we adjust the deducted scores of the track structure status indicators, enhancing the evaluation system’s accuracy in describing the working conditions. Finally, we verify the stability of the entire system by conducting a sensitivity analysis of the indicator evaluation results using the One-At-a-Time (OAT) method. This method fills a critical gap in the safe operation and maintenance of heavy-haul railways and provides a safety guarantee for the operation of long uphill sections of heavy-haul railways.

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Predicting the remaining useful life of rails based on improved deep spiking residual neural network

Cited by 2 publications

References 33 publications

Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning

Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning

A Performance Evaluation Method for Long and Steep Uphill Sections of Heavy-Haul Railway Lines

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