Technical gazette, Vol. 33 No. 4, 2026.
Original scientific paper
https://doi.org/10.17559/TV-20250625002772
Grey Neural Network-Based Demand Forecasting for Railway Freight Car Components under Condition-Based Maintenance
Yingli Hou
; School of Economics and Management, Beijing Jiaotong University, Haidian, 100044, China
Hao Hua
; China Energy Railway Equipment CO., LTD, 100009, China
Ping Gao
; China Energy Railway Equipment CO., LTD, 100009, China
Qiuli Qin
; School of Economics and Management, Beijing Jiaotong University, Haidian, 100044, China
*
* Corresponding author.
Abstract
Accurate demand forecasting of railway freight car components is critical for effective material planning under condition-based maintenance (CBM). Traditional forecasting methods often fail to capture nonlinear patterns and perform poorly with small and uncertain datasets. This paper proposes a demand forecasting model that integrates grey relational analysis with neural networks to improve prediction accuracy for key railway components. Based on historical consumption, maintenance, and market data from a major Chinese railway equipment company, influencing factors were first identified using grey correlation analysis. The selected features were then input into a grey neural network model to predict component demand. Comparative experiments show that the proposed model significantly outperforms traditional grey prediction and BP neural network approaches, with reductions in mean squared error and mean absolute error across multiple component types. The results demonstrate that grey neural networks can effectively handle small-sample, uncertain data and provide more reliable demand forecasts for CBM-driven railway operations. This study contributes to intelligent material management in railway enterprises and provides a practical reference for improving forecasting systems in complex industrial environments.
Keywords
demand forecasting; grey relational analysis; neural network; railway freight car condition-based maintenance
Hrčak ID:
348682
URI
Publication date:
30.6.2026.
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