通过原型匹配解释典型故障信号的特征
Interpreting What Typical Fault Signals Look Like via Prototype-matching
AI总结：
本文提出原型匹配网络（PMN），将自编码器特征提取与人类原型匹配机制结合，在实现机械故障诊断的同时提供分类逻辑、故障原型和匹配贡献三类解释，并展示了学习到的典型故障信号在去噪和提取细微关键特征方面的能力。
摘要：
神经网络凭借强大的非线性映射与分类能力，被广泛应用于机械故障诊断以保障安全。然而，作为典型的黑箱模型，其在高可靠性要求场景中的应用受到限制。为理解分类逻辑并解释典型故障信号的特征，本文结合人类固有的原型匹配机制与自编码器（AE），提出了原型匹配网络（PMN）。PMN将AE提取的特征与每个原型进行匹配，并选择最相似的原型作为预测结果。该网络在分类逻辑、故障原型和匹配贡献三个方面提供了解释路径。常规诊断和领域泛化实验表明，PMN具有竞争力的诊断性能以及在表示学习方面的显著优势。此外，学习到的典型故障信号（即样本级原型）展示了去噪和提取专家难以捕捉的细微关键特征的能力。这一能力拓宽了人类的理解，并为从可解释性研究到AI-for-Science提供了一种有前景的解决方案。
英文摘要：
Neural networks, with powerful nonlinear mapping and classification capabilities, are widely applied in mechanical fault diagnosis to ensure safety. However, being typical black-box models, their application is limited in high-reliability-required scenarios. To understand the classification logic and explain what typical fault signals look like, the prototype matching network (PMN) is proposed by combining the human-inherent prototype-matching with autoencoder (AE). The PMN matches AE-extracted feature with each prototype and selects the most similar prototype as the prediction result. It has three interpreting paths on classification logic, fault prototypes, and matching contributions. Conventional diagnosis and domain generalization experiments demonstrate its competitive diagnostic performance and distinguished advantages in representation learning. Besides, the learned typical fault signals (i.e., sample-level prototypes) showcase the ability for denoising and extracting subtle key features that experts find challenging to capture. This ability broadens human understanding and provides a promising solution from interpretability research to AI-for-Science.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
