多源信息融合驱动的元类增量智能迁移诊断方法

doi:10.3901/JME.260744

摘要/Abstract

摘要： 在高端装备全寿命周期服役过程中，故障模式的持续新增与演化对智能故障诊断模型的终身智能诊断能力提出了重要挑战，类增量学习驱动的终身智能诊断技术被视为保障高端装备全寿命周期运行安全的关键支撑。针对现有类增量学习方法在增量迁移场景下诊断性能受限、易受灾难性遗忘影响和模型泛化能力不足的问题，提出一种多源信息融合驱动的元类增量智能迁移诊断方法。结合连续小波变换与RGB多通道信息融合机制，融合多源传感器信号的时频特征与空间信息，以增强类增量迁移诊断场景故障样本的特征表达能力；设计一种增强型知识蒸馏方法，并与记忆回放策略协同作用，有效缓解类增量迁移诊断场景下的灾难性遗忘问题；提出一种新型元类增量参数学习策略，通过将元学习方法引入类增量学习框架，有效提升模型在类增量迁移诊断场景的泛化能力。最后，通过行星传动系统故障数据集验证了所提方法的有效性与优越性。试验结果表明：所提方法在不同类增量迁移诊断场景下的平均诊断精度达96.21%，平均遗忘率仅为1.76%，验证了所提方法实现类增量迁移诊断的优越泛化性能与强抗遗忘特性。相比现有前沿方法，所提方法的类增量迁移诊断性能表现更优，为实现高端装备全寿命周期健康管理的终身智能故障诊断提供了新解决方案。

关键词: 类增量学习, 元学习, 知识蒸馏, 信息融合, 迁移诊断

Abstract: During the full lifecycle service process of high-end equipment, the continuous emergence of new fault modes and their evolution poses great challenges to lifelong intelligent diagnosis capability of intelligent fault diagnosis models, lifelong intelligent diagnosis technology driven by class-incremental learning has been regarded as a crucial support for ensuring the operational safety of high-end equipment throughout its life cycle. To address the limitations of existing class-incremental learning methods, such as restricted diagnostic performance in class-incremental transfer scenarios, susceptibility to catastrophic forgetting, and insufficient model generalization, proposes a meta-class-incremental intelligent transfer diagnosis method driven by multi-source information fusion. The incorporation of continuous wavelet transform and RGB multi-channel information fusion mechanism is employed to integrate time-frequency features and spatial information from multi-sensor data, enhancing the feature representation capability of fault samples in class-incremental transfer diagnosis scenarios. An enhanced knowledge distillation method is designed and synergized with a memory replay strategy to effectively mitigate the catastrophic forgetting issue in class-incremental transfer diagnosis scenarios. A novel meta-class-incremental parameter learning strategy is developed by incorporating meta-learning into the class-incremental learning framework to effectively improve the model generalization in class-incremental transfer diagnosis scenarios. Finally, the effectiveness and superiority of the proposed method are validated using a planetary transmission fault dataset. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy of 96.21% and an average forgetting rate of only 1.76% across different class-incremental transfer diagnosis scenarios, thereby verifying its superior generalization ability and strong resistance to forgetting for achieving class-incremental transfer diagnosis. Compared to state-of-the-art methods, the proposed method exhibits advantageous class-incremental transfer diagnostic performance, providing a novel solution for lifelong intelligent fault diagnosis in the health management of high-end equipment throughout its full lifecycle.

Key words: class-incremental learning, meta-learning, knowledge distillation, information fusion, transfer diagnosis

中图分类号:

TH17

孔运, 林翠颖, 韩勤锴, 张春涛, 董明明, 刘辉, 褚福磊. 多源信息融合驱动的元类增量智能迁移诊断方法[J]. 机械工程学报, 2026, 62(14): 72-83.

KONG Yun, LIN Cuiying, HAN Qinkai, ZHANG Chuntao, DONG Mingming, LIU Hui, CHU Fulei. Meta-class Incremental Intelligent Transfer Diagnostic Method Driven by Multi-source Information Fusion[J]. Journal of Mechanical Engineering, 2026, 62(14): 72-83.

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http://www.cjmenet.com.cn/CN/Y2026/V62/I14/72

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