利用系统非线性生成数据，解决故障诊断中样本不足问题
Leveraging systems' non-linearity to tackle the scarcity of data in the design of Intelligent Fault Diagnosis Systems
- 通过周期性多激励激发系统非线性，生成可用于分析的图像
- 在铁路受电弓实验中实现低数据量下的高精度故障诊断
- 适合小样本场景下的工业设备智能诊断应用
深度迁移学习（DTL）可高效构建智能故障诊断系统（IFDS），但现有方法仍严重依赖大量标注数据。在机械设备故障场景下获取充足数据常面临困难。本文提出一种基于振动信号的IFDS新设计方法，在强数据稀缺条件下利用真实系统内在非线性特性，通过周期性多激励策略生成图像，便于预训练卷积神经网络（CNN）进行诊断。论文还提出一种新型数据可视化方法及其增强技术，以应对IFDS设计中常见的数据匮乏问题。在铁路受电弓结构上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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