Transfer Residual Convolutional neural Network for Rotating Machine Fault Diagnosis under Different Working Conditions

2021 ◽  
Author(s):  
Ke Zhao ◽  
Hongkai Jiang ◽  
Zhenghong Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Working Conditions ◽  
Rotating Machine ◽  
Machine Fault Diagnosis ◽  
Machine Fault

Interpretable Convolutional Neural Network Through Layer-wise Relevance Propagation for Machine Fault Diagnosis

2020 ◽  
Vol 20 (6) ◽  
pp. 3172-3181 ◽  
Author(s):  
John Grezmak ◽  
Jianjing Zhang ◽  
Peng Wang ◽  
Kenneth A. Loparo ◽  
Robert X. Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Machine Fault Diagnosis ◽  
Machine Fault

Development of a Master-Slave Architecture Handheld Rotating Machine Fault Diagnosis Instrument

2011 ◽  
Vol 141 ◽  
pp. 244-250
Author(s):  
Jian Wan ◽  
Tai Yong Wang ◽  
Jing Chuan Dong ◽  
Pan Zhang ◽  
Yan Hao
Keyword(s):  
Fault Diagnosis ◽  
Signal Integrity ◽  
Module Design ◽  
Time Performance ◽  
Rotating Machine ◽  
Machine Fault Diagnosis ◽  
Slave Processor ◽  
Design Idea ◽  
Machine Fault ◽  
Communication Methods

To insure that sampling signal integrity, accuracy and real-time performance can adapt to the development of rotating machine fault diagnosis technology, a master-slave architecture handheld rotating machine fault diagnosis instrument was developed based on S3C2410 ARM IC and TMS320VC5509A DSP IC. It provided an effective method for the field monitoring and diagnosis of the large rotating machine. The whole design idea and the structure of the hardware and the software were systematically introduced. The paper focused on the master-slave architecture design of the hardware, the communication methods between the master and the slave processor, and the signal pretreatment module design. Put into practice, the practicability, reliability and stability of the instrument were confirmed.

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