An advanced neural-network-based instrument fault detection and isolation scheme

1998 ◽  
Vol 47 (2) ◽  
pp. 507-512 ◽  
Author(s):  
G. Betta ◽  
C. Liguori ◽  
A. Pietrosanto
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Fault Detection And Isolation

LRE Fault Detection and Isolation Based on Fuzzy Direction Neural Network

2015 ◽  
Vol 727-728 ◽  
pp. 880-883
Author(s):  
Min Chao Huang ◽  
Bao Yu Xing
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Rocket Engine ◽  
Fault Detection And Isolation ◽  
Liquid Rocket Engine ◽  
Direction Vector ◽  
Liquid Rocket ◽  
Turbo Pump

A fuzzy directions neural network used for fault detection and isolation (FDI) of a liquid rocket engine (LRE) is presented in this paper. Neural network utilizes fuzzy sets as engine fault classes. Each fuzzy set is an aggregate of fuzzy direction bodies. A fuzzy direction body is described by a direction vector, an included angle and two radii. FDI simulation of the turbo-pump fed liquid rocket engine demonstrates the strong qualities of the fuzzy direction neural network.

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.

Sign in / Sign up

Export Citation Format

Share Document