Deep learning and its applications to machine health monitoring

By Rui Zhao, Ruqiang Yan, Zhenghua Chen, Kezhi Mao, Peng Wang, Robert X. Gao. Mechanical systems and signal processing, 2019

2,506 citations recorded by the source corpus.

Abstract 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). In addition, an experimental study on the performances of these approaches has been conducted, in which the data and code have been online. Finally, some new trends of DL-based machine health monitoring methods are discussed.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/1ed33896e82cc3810b349cdffc2039f0b8edd82e

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Industrial and Manufacturing Engineering

Citations in the corpus

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, neutral): “[11,12] discussed AI for machine condition monitoring and fault diagnosis, while Ref.”
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, methodology): “For example, a simple code written in Keras was published in [21], which is not comprehensive enough for different datasets and models.”
Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “As stated in [24] , the features produced by the hidden lay-”
Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning (2019, methodology): “Deep learning methods have also been applied to fault diagnosis and obtained state-of-the-art results [6] using techniques such as auto-encoders (AE) [12]–[15], restricted boltzmann machine (RBM) [16]–[19], convolutional neural networks (CNNs) [20]–[24], recurrent neural”
Digital twin, physics-based model, and machine learning applied to damage detection in structures (2020, background): “Zhao et al [12] apply deep learning (auto-encoder, convolution networks, etc.) in machine health monitoring systems.”
Parallel spatio-temporal attention-based TCN for multivariate time series prediction (2021, background): “Complex systems are commonplace in today’s manufacturing plants [1], health monitoring [2] and ensuring these systems run smoothly inevitably involves constant monitoring of numerous diverse data streams, from temperature and pressure sensors to image and video feeds to CPU usage levels, biometric…”
DCNN-Based Multi-Signal Induction Motor Fault Diagnosis (2020, background): “There are several typical deep architectures that have been successfully applied to machine fault diagnosis [29]–[32], including autoencoders (AE) [33], deep belief network (DBN) [34], and convolutional neural network (CNN) [35].”
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, background): “algorithms, has made almost unimaginable advances in AI and technology industries [20].”