摘要
维保时机强调装备停机的主动性,是在性能退化达到预设值前,结合工程节奏合理安排停机检修。该任务的精准预测对装备可靠运行至关重要,但仍面临多源数据融合、退化特征量化难及长依赖学习等挑战。因此提出一种基于退化感知时序建模的装备维保时机预测方法,以动态表征装备连续运行过程中的性能退化,并自适应捕获多传感器数据间的深层依赖关系。首先,提出性能退化指标(PDI),通过时序数据驱动的性能量化器,实现动态的装备性能衰减感知;然后,构建基于多头注意力机制与序列到序列的维保时机预测模型,以自适应学习多源特征的相关性;最后,融合退化感知参数以强化特征权重分配,提升模型对装备长期运行趋势的预测能力。实验结果表明,融合PDI后模型最佳性能提升近13.5%,在隧道掘进机(TBM)工程数据集上较标准长短期记忆网络(LSTM)的均方根误差(RMSE)提升约25%,相比其他模型提升近15%以上,实现了较高的预测精度。在C-MAPSS数据集上与循环神经网络(RNN)和图神经网络(GNN)及注意力机制等主流时序预测方法进行了对比验证,结果表明该方法在维保时机预测任务中表现最优,并详细分析不同传感器数量对模型性能的影响。此外,该方法具备良好的可扩展性,可进一步融合装备运行环境信息感知,为装备的智能运维决策与操控闭环提供技术支撑。
Maintenance-interval prediction focuses on the proactive scheduling of equipment downtime,arranging maintenance before performance degradation reaches a predefined threshold while aligning with engineering operations.Accurate prediction of such intervals is vital for reliable equipment operation but remains challenging due to difficulties in multi-source data fusion,quantitative degradation characterization,and long-term dependency learning.This study presented a degradation-driven temporal modeling method that dynamically represented performance deterioration during continuous operation and adaptively captured complex dependencies among multi-sensor data.A performance-degradation indicator(PDI)quantified equipment performance decline using time-series measurements.To capture correlations among multi-source features,a sequence-to-sequence prediction model with multi-head attention was constructed and degradation-aware parameters were integrated,which optimized feature weighting and improved long-term trend prediction.The experimental results indicated that the optimal performance of the model improved by nearly 13.5%after integrating PDI.On the TBM(tunnel boring machine)engineering dataset,an RMSE(root mean square error)improvement of approximately 25%was achieved compared to the standard LSTM(long short-term memory),and outperformed other models by nearly 15%,yielding high prediction accuracy.Further evaluation on the C-MAPSS dataset against RNN(recurrent neural network),GNN(graph neural network),and attention-based methods confirmed the approach’s effectiveness,offering a detailed analysis of how varying the number of sensors affected model performance.The method also exhibited strong scalability and could be extended to incorporate environmental-condition awareness,providing technical support for intelligent maintenance decision-making and closed-loop operational control.
作者
薄文
琚晨
刘维青
张焱
胡晶晶
程婧晗
张常有
BO Wen;JU Chen;LIU Weiqing;ZHANG Yan;HU Jingjing;CHENG Jinghan;ZHANG Changyou(School of Computer Science&Technology,Beijing Institute of Technology,Beijing 100081,China;Institute of Software,Chinese Academy of Sciences,Beijing 100190,China;China Railway 19 Bureau Group Co.,Ltd.,Beijing 100176,China;School of Software,Xinjiang University,Ürümqi Xinjiang 830046,China)
出处
《图学学报》
北大核心
2025年第6期1233-1246,共14页
Journal of Graphics
基金
国家重点研发计划(2023YFB3611303)
中华人民共和国水利部重大项目(SKS-2022104)。
关键词
维保时机预测
多头注意力机制
序列到序列
深度学习
智能运维
maintenance interval prediction
multi-head attention mechanism
sequence-to-sequence model
deep learning
intelligent maintenance systems
