Abstract
Machine systems inherently generate signals in which fault conditions and various physical variables are physically coupled. Although many existing fault classification studies rely solely on direct fault labels, the aforementioned signals naturally embed additional information shaped by other physically coupled information. Herein, we leverage this coupling through a multi-task learning (MTL) framework that jointly learns fault conditions and the related physical variables. Among MTL architectures, crosstalk structures have distinct advantages because they allow for controlled information exchange between tasks through the cross-talk layer while preventing negative transfer, in contrast to shared trunk architectures that often mix incompatible features. We build on our previously introduced residual neural dimension reductor model, and extend its application to two benchmarks where physical coupling is prominent. The first benchmark is a drone fault dataset, in which machine type and maneuvering direction significantly alter the frequency components of measured signals even under the same nominal condition. By learning fault classification together with these physical attributes, the cross-talk architecture can better classify faults. The second benchmark dataset is the motor compound fault dataset. In this system, each fault component, inner race fault, outer race fault, misalignment, and unbalance is coupled to the other. For motor compound fault, we also test classification performance when we use single-channel data or multi-channel data as input to the classifier. Across both benchmarks, our residual neural dimension reductor, consistently outperformed single-task models, multi-class models that merge all label combinations, and shared trunk multi-task models.
Abstract (translated)
机器系统内在地会产生信号，在这些信号中，故障条件和各种物理变量是通过物理方式耦合在一起的。尽管许多现有的故障分类研究仅依赖于直接的故障标签，但上述信号自然嵌入了其他物理相关信息塑造的额外信息。在此基础上，我们利用这种耦合关系并通过一个多任务学习（MTL）框架同时学习故障条件及其相关的物理变量。在多任务架构中，交叉结构具有明显的优点，因为它们允许通过交叉层进行受控的信息交换，从而防止负向迁移，而与之相对的共享主干架构往往混合了不兼容的特征。 我们在此基础上扩展了之前介绍的残差神经维度缩减模型，并将其应用到两个突出物理耦合的重要基准数据集上。第一个基准是无人机故障数据集，在同一名义条件下，机器类型和操控方向会显著改变测量信号中的频率成分。通过同时学习故障分类及这些物理属性，交叉架构能够更好地进行故障分类。 第二个基准数据集是电机复合故障数据集。在这个系统中，每个故障组件（内圈故障、外圈故障、不对中和不平衡）都与其他组件耦合在一起。对于电机复合故障，我们还测试了单通道输入或多个通道输入到分类器时的分类性能表现。 在这两个基准上，我们的残差神经维度缩减模型始终优于单一任务模型、合并所有标签组合的多类别模型以及共享主干架构的多任务模型。
URL
https://arxiv.org/abs/2602.05146
