scholarly journals Fault Detection and Isolation in Electrical Machines using Deep Neural Networks

2019 ◽  
Vol 69 (3) ◽  
pp. 249-253
Author(s):  
M. Sai ◽  
Parth Upadhyay ◽  
Babji Srinivasan
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Health Monitoring ◽  
Time Domain ◽  
Deep Neural Networks ◽  
Experimental Studies ◽  
Electrical Machines ◽  
Fault Detection And Isolation ◽  
Challenging Problem ◽  
Original Time

Condition and health monitoring of electrical machines during dynamic loading is a common, yet challenging problem in main battle tanks. Existing methods address this issue by extracting various features which are subsequently used in a classifier to isolate faults. However, this approach relies on the feature set being extracted and therefore most of the time does not provide expected accuracy in identification of faults. In this work, we have used convolution neural network that utilises the original time domain measurements for fault detection and isolation (FDI). Results from experimental studies indicate that the proposed approach can perform FDI with more than 95\% accuracy using commonly available current measurements.

scholarly journals Data Challenges for Structural Health Monitoring of Electrical Machines

2021 ◽  
Author(s):  
Alex Binder ◽  
Conner Ozatalar ◽  
Kendyl Wright ◽  
Nicholas Lievin-Lieven ◽  
Phillip Cornwell
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Electrical Machines ◽  
Structural Health

Wideband Fiber Bragg Grating Accelerometer Suitable for Health Monitoring of Electrical Machines

2020 ◽  
Vol 20 (24) ◽  
pp. 14865-14872
Author(s):  
Nandini Basumallick ◽  
Sayantani Bhattacharya ◽  
Tanoy Kumar Dey ◽  
Palas Biswas ◽  
Somnath Bandyopadhyay
Keyword(s):  
Fiber Bragg Grating ◽  
Health Monitoring ◽  
Electrical Machines ◽  
Bragg Grating

Health Monitoring Measure

2007 ◽  
Author(s):  
Katherine Harris Abbott ◽  
Eleanor Palo Stoller ◽  
Julia Hannum Rose
Keyword(s):  
Health Monitoring
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