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首页> 外文期刊>Mathematical Problems in Engineering >A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis
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A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis

机译：基于堆叠式自动编码器的深度神经网络可实现变速箱故障诊断

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摘要

Machinery fault diagnosis is pretty vital in modern manufacturing industry since an early detection can avoid some dangerous situations. Among various diagnosis methods, data-driven approaches are gaining popularity with the widespread development of data analysis techniques. In this research, an effective deep learning method known as stacked autoencoders (SAEs) is proposed to solve gearbox fault diagnosis. The proposed method can directly extract salient features from frequency-domain signals and eliminate the exhausted use of handcrafted features. Furthermore, to reduce the overfitting problem in training process and improve the performance for small training set, dropout technique and ReLU activation function arc introduced into SAEs. Two gearbox datasets are employed to conform the effectiveness of the proposed method; the result indicates that the proposed method can not only achieve significant improvement but also is superior to the raw SAEs and some other traditional methods.
机译：机械故障诊断在现代制造业中至关重要，因为及早发现可以避免某些危险情况。在各种诊断方法中，数据驱动方法随着数据分析技术的广泛发展而变得越来越流行。在这项研究中，提出了一种有效的深度学习方法，称为堆叠式自动编码器（SAE），用于解决变速箱故障诊断。所提出的方法可以直接从频域信号中提取显着特征，并消除了手工特征的穷举使用。此外，为了减少训练过程中的过度拟合问题并提高小型训练集的性能，将脱节技术和ReLU激活功能引入了SAE。采用两个齿轮箱数据集来验证所提出方法的有效性。结果表明，所提出的方法不仅可以显着改善，而且优于原始的SAE和其他一些传统方法。

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