关键词: 深度学习, 特征提取, 故障诊断, 故障预测

Abstract: In recent years, deep learning has been widely applied and has made remarkable progress in many fields because of its unique advantages and potential in feature extraction and pattern recognition. Its application in fault diagnosis and prognosis of complex industrial systems is an emerging field. This paper starts with an overview of deep learning, including deep learning methods-based application, platforms and useful tools. Five frequently-used deep learning models are introduced in this work, including Auto-Encoder（AE）, Deep Belief Networks（DBN）, Convolutional neural networks（CNN）, Recurrent Neural Network（RNN） and Generative Adversarial Network（GAN）. The application research based on deep learning in fault diagnosis and prognosis are systematically discussed in three aspects, research background, implementation process and research dynamics, and the current related literatures published in this field in recent years are reviewed. Problems, challenges and solutions of deep learning in the application of fault diagnosis and prognosis are discussed from the point view of research practice. The future research directions are also prospected at the end of this work.

Key words: deep learning, feature extraction, fault diagnosis, fault prognosis