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首页> 外文期刊>IEEE transactions on industrial informatics >Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample
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Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample

机译：齿轮箱故障诊断使用具有有限数据样本的深层学习模型

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摘要

Massive volumes of data are needed for deep learning (DL) models to provide accurate diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the effectiveness of DL models over shallow machine learning (SL) in terms of feature extraction, feature dimensional reduction and diagnosis performance. Occasionally, during data acquisition, a problem with a sensor renders some of the data potentially unsuitable for further analysis, leaving only a small data sample. To compensate for this deficiency, a DL model based on a stacked sparse autoencoder (SSAE) model is designed to deal with limited sample data. In this article, the fault diagnosis system is developed based on time-frequency image pattern recognition. Therefore, two gearbox datasets are used to evaluate the proposed diagnosis system. The results from the experiments prove that the proposed system is capable of achieving high diagnostic accuracy even with limited sample data. The proposed fault diagnosis system achieved 100% and 99% diagnosis performance on experimental gearbox and wind turbine gearbox datasets, respectively. The proposed diagnosis system increased diagnosis performance between 10% and 20% over the standard SSAE model. In addition, the proposed model achieved higher diagnosis performance compared to deep neural network and convolutional neural networks models.
机译：深度学习（DL）模型需要大量数据，以提供准确的诊断结果。在特征提取方面，许多对故障诊断系统的研究表明DL模型在浅机器学习（SL）上的有效性，特征尺寸减少和诊断性能。偶尔，在数据采集期间，传感器的问题呈现一些可能不适合进一步分析的数据，仅留下小数据样本。为了弥补这种缺陷，设计了一种基于堆积的稀疏自动化器（SSAE）模型的DL模型，用于处理有限的示例数据。在本文中，基于时频图像模式识别开发故障诊断系统。因此，两个变速箱数据集用于评估所提出的诊断系统。实验结果证明，即使具有有限的样本数据，所提出的系统也能够实现高诊断准确性。所提出的故障诊断系统分别在实验齿轮箱和风力涡轮机齿轮箱数据集上实现了100％和99％的诊断性能。在标准SSAE模型上，所提出的诊断系统增加了10％至20％的诊断性能。此外，与深神经网络和卷积神经网络模型相比，所提出的模型实现了更高的诊断性能。

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