基于ARIMA-SVR模型的轨道交通车辆关键设备检修偶换件数量预测
王玥龙1,2刘鹏1,2姚伟君2
Forecasting Spare Parts Demand for Critical Rail Transit Vehicle Equipment Based on ARIMA-SVR Model
WANG Yuelong1,2LIU Peng1,2YAO Weijun2
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作者信息:1.中国铁道科学研究院集团有限公司机车车辆研究所，100081，北京；
2.北京纵横机电科技有限公司，100094，北京
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Affiliation:1.Locomotive & Car Research Institute, China Academy of Railway Sciences Co., Ltd., 100081, Beijing, China
2.Beijing Zongheng Electro-Mechanical Technology Co., Ltd., 100094, Beijing, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2025.03.046
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中图分类号/CLCN:U270.38
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栏目/Col:应用技术
摘要:
［目的］准确预测轨道交通车辆关键设备检修偶换件数量，可为科学的备件管理提供依据，提高检修经济性。但是现有预测方法准确性不足，预测效果差，因此有必要针对检修偶换件数量预测问题进行深入研究。［方法］根据轨道交通车辆设备检修偶换件数据的特性，构建了检修偶换率(即偶换件更换比例)和检修量的月度时间序列。通过深入研究时间序列预测算法，并对比各类预测算法的效果，综合考虑准确性与泛化能力，提出了一种结合ARIMA(自回归综合移动平均法)与SVR(支持向量回归算法)的计算方法。首先利用ARIMA进行偶换率的预测，然后运用SVR进行检修量的预测，最后结合偶换率与检修量的预测结果来计算偶换件的预测数量。此外，还结合了ARIMA预测的置信区间与无监督聚类IForest(孤立森林)算法，提出了一种偶换率异常检测方法。［结果及结论］以高度阀和制动夹钳单元这两种典型产品的高级修数据为例，对所提出的预测方法进行了验证计算。结果表明，与现有的历史平均法相比，该方法的预测准确性得到了显著提升，并且能够有效地检测出历史和当前的检修偶换率异常情况。
Abstracts:
［Objective］ Accurately forecasting the demand for spare parts during critical rail transit vehicle equipment maintenance provides a foundation for scientific spare parts management and improves maintenance cost-efficiency. However, existing forecasting methods lack accuracy and exhibit poor performance. Thus, an in-depth study of spare parts demand forecasting in maintenance is necessary. ［Method］ Based on the characteristics of spare parts data for rail transit vehicle maintenance, monthly time series are constructed for the replacement rate (i.e., the proportion of parts replaced) and maintenance amount. After thoroughly investigating time series forecasting algorithms and comparing their performance, comprehensively considering their accuracy and generalization, a combined method leveraging ARIMA (autoregressive integrated moving average) and SVR (support vector regression) is proposed. ARIMA is used to predict replacement rates, while SVR is applied to forecast maintenance amount. Finally, the spare parts forecast is calculated by combining the prediction results of both. Additionally, an anomaly detection method for replacement rates is proposed by integrating ARIMA′s confidence intervals with the unsupervised clustering algorithm IForest (Isolation Forest). ［Result & Conclusion］ Taking advanced maintenance data of height valve and brake caliper unit two typical products as example, validation and calculation of the proposed forecasting method is conducted. Results show that compared to the existing historical average method, the proposed method significantly improves forecasting accuracy and effectively detects anomalies in both historical and current replacement rates during maintenance.
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