Electrical Pulsed Infrared Thermography and supervised learning for PV cells defects detection

2022 ◽  
Vol 237 ◽  
pp. 111561
Author(s):  
Chiwu Bu ◽  
Tao Liu ◽  
Rui Li ◽  
Runhong Shen ◽  
Bo Zhao ◽  
...  
2020 ◽  
Vol 104 ◽  
pp. 103074 ◽  
Author(s):  
Chiwu Bu ◽  
Guozeng Liu ◽  
Xibin Zhang ◽  
Qingju Tang

2019 ◽  
Vol 9 (2) ◽  
pp. 142-150 ◽  
Author(s):  
Zhengwei Yang ◽  
Guangjie Kou ◽  
Yin Li ◽  
Gan Tian ◽  
Wei Zhang ◽  
...  

2009 ◽  
Author(s):  
Hernán D. Benítez ◽  
Clemente Ibarra-Castanedo ◽  
AbdelHakim Bendada ◽  
Xavier Maldague

2011 ◽  
Author(s):  
Yan Huo ◽  
Hui-Juan Li ◽  
Yue-Jin Zhao ◽  
Cun-Lin Zhang

2015 ◽  
Vol 72 ◽  
pp. 90-94 ◽  
Author(s):  
Liu Yuanlin ◽  
Tang Qingju ◽  
Bu Chiwu ◽  
Mei Chen ◽  
Wang Pingshan ◽  
...  

2005 ◽  
Vol 98 (10) ◽  
pp. 103502 ◽  
Author(s):  
Florencio Garrido ◽  
Agustín Salazar ◽  
Fernando Alonso ◽  
Idurre Sáez-Ocáriz

2020 ◽  
Vol 10 (2) ◽  
pp. 506 ◽  
Author(s):  
Emmanuel Resendiz-Ochoa ◽  
Juan J. Saucedo-Dorantes ◽  
Juan P. Benitez-Rangel ◽  
Roque A. Osornio-Rios ◽  
Luis A. Morales-Hernandez

In gearboxes, the occurrence of unexpected failures such as wear in the gears may occur, causing unwanted downtime with significant financial losses and human efforts. Nowadays, noninvasive sensing represents a suitable tool for carrying out the condition monitoring and fault assessment of industrial equipment in continuous operating conditions. Infrared thermography has the characteristic of being installed outside the machinery or the industrial process under assessment. Also, the amount of information that sensors can provide has become a challenge for data processing. Additionally, with the development of condition monitoring strategies based on supervised learning and artificial intelligence, the processing of signals with significant improvements during the classification of information has been facilitated. Thus, this paper proposes a novel noninvasive methodology for the diagnosis and classification of different levels of uniform wear in gears through thermal analysis with infrared imaging. The novelty of the proposed method includes the calculation of statistical time-domain features from infrared imaging, the consideration of a dimensionality reduction stage by means of Linear Discriminant Analysis, and automatic fault diagnosis performed by an artificial neural network. The proposed method is evaluated under an experimental laboratory data set, which is composed of the following conditions: healthy, and three severity degrees of uniform wear in gears, namely, 25%, 50%, and 75% of uniform wear. Finally, the obtained results are compared with classical condition monitoring approaches based on vibration analysis.


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