深度学习在轴承故障诊断领域的应用研究

深度学习具有强大的学习能力和特征分类能力,能够在海量、多源和高维测量数据中进行特征提取,具有不依赖人工干预而进行模型诊断和泛化的能力,广泛应用于设备故障诊断领域。阐述了深度学习的典型模型:深度置信网络(DBN)、卷积神经网络(CNN)和自编码器(AE),重点论述了深度学习在轴承故障诊断领域的应用进展。最后讨论了深度学习在轴承故障诊断领域所存在的问题及发展趋势。

Application of Deep Learning in Bearing Fault Diagnosis

With strong learning ability, feature classification ability, and generalization capabilities, deep learning could be used in the feature extraction and model diagnosis based on massive, multi-source and high-dimensional measurement data, with no reliance on human intervention, and it can be widely used in the field of equipment fault diagnosis. The typical models of deep learning (Deep Belief Network (DBN), Convolutional Neural Network (CNN) and Automatic Encoders (AE)) were described, and the application progress of deep learning in bearing fault diagnosis was emphasized. Finally, the problems and development trends of deep learning in the field of bearing fault diagnosis were discussed.