Deep learning models, including Auto-encoders, RBMs, CNNs, and RNNs, provide effective end-to-end solutions for data-driven machine health monitoring without requiring hand-crafted features.
This survey provides a comprehensive overview of deep learning applications in data-driven machine health monitoring.
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.
Zhao et al. (2016) conducted a review in Machine health monitoring. Deep learning vs. Conventional data-driven methods was evaluated. Deep learning models, including Auto-encoders, RBMs, CNNs, and RNNs, provide effective end-to-end solutions for data-driven machine health monitoring without requiring hand-crafted features.
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