一种基于条件度量迁移学习的机械故障诊断可解释方法

Interpretable method for mechanical fault diagnosis based on condition metric transfer learning

迁移学习技术可以减小源域和目标域特征之间的分布差异。然而,在跨设备场景下,现有研究往往难以衡量并缩小不同设备间数据的条件分布差异,导致模型在源域获得的知识不能很好地迁移到目标域。此外,在实际的故障诊断场景中,决策者通常需要理解模型为何将某些数据归类为特定故障类型。由于深度学习模型的复杂性,其往往被看作是“黑匣子”,难以解释其内部工作机制。为了克服上述缺点,提出一种基于条件度量迁移学习的可解释故障诊断方法。首先利用Hilbert包络谱分析将时域信号转为频域信号;其次搭建深度孪生卷积神经网络和分类器,从频域中提取源域和目标域数据中的高维特征并做分类训练;然后将可解释的条件核Bures度量嵌入到无监督学习的损失函数中,提高条件分布下的特征适配能力和模型可解释性;最后利用博弈论中的SHAP方法对模型诊断结果做基于包络谱的事后可解释分析。在3种设备的6种跨设备轴承故障诊断任务中开展试验,对所提方法和其他相关对比方法进行评估,结果表明提出的方法可以有效地提高跨设备机械故障诊断精度,达到了平均99.47%的诊断精度。并解释了哪些频率点对模型的决策起到关键作用。

Transfer learning techniques can reduce the distribution difference between source and target domain features.However,in cross-device scenarios,existing research is often difficult to measure and reduce the differences in the conditions of data between different devices,resulting in the knowledge obtained by the model in the source domain cannot be migrated to the target domain.Additionally,in real-world failure diagnostic scenarios,decision-makers usually need to understand why the model classifies a specific type of fault.Due to the complexity of deep learning models,they are often seen as"black boxes,"making it difficult to explain their internal workings.To address these issues,an interpretable fault diagnosis method based on conditional metric transfer learning is proposed.Firstly,Hilbert envelope spectrum analysis is used to convert time-domain signals into frequency-domain signals.Secondly,a deep twin convolutional neural network and classifier are built to extract high-dimensional features from both source and target domain data in the frequency domain and perform classification training.Then,the interpretable Conditional Kernel Bures is embedded into the loss function of unsupervised learning to enhance feature adaptation and model interpretability under conditional distribution.Finally,the SHAP method from game theory is used to conduct post-event interpretable analysis of the model diagnosis results based on the envelope spectrum.Tests were conducted on 12 cross-equipment bearing fault diagnosis tasks across three types of mechanical equipment,evaluating the proposed method against other related methods.The results show that the proposed method could effectively improve the accuracy of cross-equipment mechanical fault diagnosis,achieving an average diagnostic accuracy of 99.47%.It also identifies which frequency points played a crucial role in the model′s decision-making process.