这篇论文用系统非线性生成图像，数据极少也能做故障诊断，还在铁路受电弓上验证了，搞工业AI的可以看看。
论文提出一种在数据极度稀缺条件下设计基于深度迁移学习的振动智能故障诊断系统（IFDS）的新方法。该方法采用周期性多激励水平程序，利用真实系统的固有非线性特性生成图像，供预训练卷积神经网络（CNN）分析以诊断故障。同时提出一种新的数据可视化及增强技术以应对IFDS设计中典型的数据不足。在铁路受电弓结构上的实验验证了该方法的有效性。
Leveraging systems' non-linearity to tackle the scarcity of data in the design of Intelligent Fault Diagnosis Systems
Deep Transfer Learning (DTL) allows for the efficient building of Intelligent Fault Diagnosis Systems (IFDS). On the other hand, DTL methods still heavily rely on large amounts of labelled data. Obtaining such an amount of data can be challenging when dealing with machines or structures faults. This document proposes a novel approach to the design of vibration-based IFDS using DTL in condition of strong data scarcity. A periodic multi-excitation level procedure leveraging intrinsic non-linearities of real-world systems is used to produce images that can be conveniently analysed by pre-trained Convolutional Neural Networks (CNNs) to diagnose faults. A new data visualization method and its augmentation technique are proposed in this paper to tackle the typical lack of data encountered during the design of IFDS. Experimental validation on a railway pantograph structure provides effective support for the proposed method.