A novel minority sample fault diagnosis method based on multisource data enhancement

IF 3.4 Q1 ENGINEERING, MECHANICAL
Yiming Guo, Shida Song, Jing Huang
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引用次数: 0

Abstract

Effective fault diagnosis has a crucial impact on the safety and cost of complex manufacturing systems. However, the complex structure of the collected multisource data and scarcity of fault samples make it difficult to accurately identify multiple fault conditions. To address this challenge, this paper proposes a novel deep-learning model for multisource data augmentation and small sample fault diagnosis. The raw multisource data are first converted into two-dimensional images using the Gramian Angular Field, and a generator is built to transform random noise into images through transposed convolution operations. Then, two discriminators are constructed to evaluate the authenticity of input images and the fault diagnosis ability. The Vision Transformer network is built to diagnose faults and obtain the classification error for the discriminator. Furthermore, a global optimization strategy is designed to upgrade parameters in the model. The discriminators and generator compete with each other until Nash equilibrium is achieved. A real-world multistep forging machine is adopted to compare and validate the performance of different methods. The experimental results indicate that the proposed method has multisource data augmentation and minority sample fault diagnosis capabilities. Compared with other state-of-the-art models, the proposed approach has better fault diagnosis accuracy in various scenarios.

Abstract Image

基于多源数据增强的新型少数样本故障诊断方法
有效的故障诊断对复杂制造系统的安全性和成本有着至关重要的影响。然而,收集到的多源数据结构复杂,故障样本稀少,因此很难准确识别多种故障情况。为应对这一挑战,本文提出了一种新型深度学习模型,用于多源数据增强和小样本故障诊断。首先利用格拉米安角场将原始多源数据转换为二维图像,并建立一个生成器,通过转置卷积操作将随机噪声转换为图像。然后,构建两个判别器来评估输入图像的真实性和故障诊断能力。建立视觉变换器网络来诊断故障,并获得判别器的分类误差。此外,还设计了一种全局优化策略来升级模型中的参数。鉴别器和发生器相互竞争,直至达到纳什平衡。实验采用了真实世界中的多步锻造机来比较和验证不同方法的性能。实验结果表明,所提出的方法具有多源数据增强和少数样本故障诊断能力。与其他最先进的模型相比,所提出的方法在各种情况下都具有更高的故障诊断精度。
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CiteScore
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