Deep Learning and Its Applications to Machine Health Monitoring: A Survey
Deep Learning and Its Applications to Machine Health Monitoring: A Survey
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发表时间：
2016-12
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通讯作者：
Rui Zhao;Ruqiang Yan;Zhenghua Chen;K. Mao;Peng Wang;R. Gao
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作者：
Rui Zhao;Ruqiang Yan;Zhenghua Chen;K. Mao;Peng Wang;R. Gao
自2006年以来，深度学习已成为一个迅速发展的研究方向，重新定义了对象识别、图像分割、语音识别和机器翻译等广泛领域的最先进性能。在现代制造系统中，由于低成本传感器的广泛部署及其与互联网的连接，数据驱动的机器健康监测越来越受欢迎。同时，深度学习为处理和分析这些大数据提供了有用的工具。本文的主要目的是回顾和总结机器健康监测领域中新兴的深度学习研究工作。在简要介绍深度学习技术的基础上，主要从自动编码器(AE)及其变体、受限Boltzmann机器及其变体包括深度信念网络(DBN)和深度Boltzmann机器(DBM)、卷积神经网络(CNN)和递归神经网络(RNN)等方面综述了深度学习在机器健康监测系统中的应用。最后对基于动态链接库的机器健康监测方法的发展趋势进行了展望。
Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Finally, some new trends of DL-based machine health monitoring methods are discussed.