非理想数据下基于时序对比注意力模型的寿命预测方法

doi:10.3901/JME.2025.18.027

摘要/Abstract

摘要： 在监测大数据背景下，理想数据的获取往往受到限制，高价值数据稀缺性导致传统智能预测模型泛化性弱、准确率低。为此，提出一种时序对比学习与注意力机制的联合深度学习框架，有效利用片段式的、无完整寿命标签的非理想数据实现设备剩余寿命预测。首先构建时序对比编码器，在无需寿命标签引导下自动注释非理想数据的深度退化特征，并增强所提取特征的时间自相关性。其次构建寿命预测注意力解码器，对编码后的退化特征进行动态权重分配与并行计算拟合。在此基础上，提出基于弹性权重共享的编码-解码训练优化模式，实现编码器与解码器的有机结合与高效交互。通过轴承与轨交列车轮对全寿命试验实例，验证所提方法的有效性。结果表明，所提方法在两种应用场景下的预测精度均优于对比方法，平均预测精度提升约34%。

关键词: 剩余寿命预测, 时序对比学习, 注意力机制, 轴承, 轨交列车轮对

Abstract: In the context of monitoring big data, the acquisition of ideal data is often constrained, resulting in a scarcity of high-value data. Consequently, traditional intelligent prediction models are characterized by weak generalizability and low accuracy. To address this issue, a sequential contrastive attention network (SCAN) is proposed to effectively utilize fragmentary, non-ideal data without complete lifespan labels for predicting the remaining useful life of equipment. Initially, a sequential contrastive encoder is constructed to automatically annotate the deep degradation features of non-ideal data without requiring lifespan labels, enhancing the temporal autocorrelation of the extracted features. Subsequently, a lifespan prediction attention decoder is designed to dynamically allocate weights to the encoded degradation features and perform parallel computation fitting. On this basis, an encoding-decoding training optimization mode based on elastic weight sharing is introduced to achieve an organic combination and efficient interaction between the encoder and decoder. The effectiveness of the proposed method is verified using full lifespan test instances of bearings and urban rail transit train wheels. Results demonstrate that the proposed method achieves superior prediction accuracy compared to other approaches, with an average accuracy improvement of approximately 34% in both application scenarios.

Key words: remaining useful life prediction, sequential contrastive learning, attention mechanism, bearings, urban rail transit train wheelset

中图分类号:

TG156

林天骄, 宋浏阳, 崔玲丽, 王华庆. 非理想数据下基于时序对比注意力模型的寿命预测方法[J]. 机械工程学报, 2025, 61(18): 27-37.

LIN Tianjiao, SONG Liuyang, CUI Lingli, WANG Huaqing. Predicting the Remaining Useful Life of Non-ideal Data Through a Joint Framework of Sequential Contrastive Learning and Attention Mechanisms[J]. Journal of Mechanical Engineering, 2025, 61(18): 27-37.

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