Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds

2022 ◽  
Vol 62 ◽  
pp. 186-198
Author(s):  
Hongru Cao ◽  
Haidong Shao ◽  
Xiang Zhong ◽  
Qianwang Deng ◽  
Xingkai Yang ◽  
...  
Keyword(s):  
Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


2016 ◽  
Author(s):  
Felix Schindler ◽  
Bertram Steininger ◽  
Tim Kroencke

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