Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

By Min Xia, Teng Li, Lin Xu, Lizhi Liu, C. D. de Silva. IEEE/ASME transactions on mechatronics, 2018

614 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/55d8406b42ab7a831a184ab635945558e82ace95

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Dhiraj Neupane
2. Jong-Hoon Seok
3. Liang Gao
4. Xinyu Li
5. Long Wen
6. Shen Zhang
7. Bingnan Wang
8. Shibo Zhang

Citations in the corpus

• Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks (2020, methodology): “CNN, deep belief network (DBN), stacked autoencoder (SAE), and other deep learning models [17]–[22]”
• Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, methodology): “60% [108] CNN 4 Softmax Sensor fusion 70% 99.”
• Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, methodology): “In [89], a CNN-based approach with multiple sensor fusion is proposed for fault diagnosis of rotating machinery in which both spatial and temporal raw data from the drive-end and fan-end are stacked by converting 1-”
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “60% [107] CNN 4 Softmax Sensor fusion 70% 99.”
• A transfer convolutional neural network for fault diagnosis based on ResNet-50 (2019, methodology): “…[31], hierarchical convolutional network (CNN-1) form Lu et al. [32], convolutional neural network and empirical mode decomposition (CNN-2) from Xie et al. [33] and CNN using multiple sensors (CNN-3) from Xia et al. [34] The comparison results of TCNN(ResNet-50) are presented in Tables 9 and 10.”
• Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis (2020, methodology): “D CNN (2DCNN) to implement fault diagnosis [15], [18], [20], [25]–[27].”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “17. Simple illustration from mechanical data to image. Image transformation means that researchers try to convert 1-D mechanical signals into the image in pixel format as shown in Fig. 17. Xia et al. [53] presented a fault diagnosis method for rotating machinery based on multiple sensors fusion and ”