Fault Detection and Diagnosis with A Novel Source-Aware Autoencoder and Deep Residual Neural Network

2021 ◽  
Author(s):  
Nima Amini ◽  
Qinqin Zhu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Detection And Diagnosis

Fault detection and diagnosis for reactive distillation based on convolutional neural network

2021 ◽  
Vol 145 ◽  
pp. 107172
Author(s):  
Xiaolong Ge ◽  
Beibei Wang ◽  
Xinchuang Yang ◽  
Yu Pan ◽  
Botan Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Reactive Distillation ◽  
Fault Detection And Diagnosis ◽  
Detection And Diagnosis

Detection and Diagnosis of Broken Rotor Bars in Induction Motors Using the Fuzzy Min-Max Neural Network

2012 ◽  
Vol 3 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Manjeevan Seera ◽  
Chee Peng Lim ◽  
Dahaman Ishak
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Induction Motors ◽  
Fault Detection And Diagnosis ◽  
Broken Rotor Bars ◽  
Power Spectral ◽  
Motor Current Signature Analysis ◽  
Detection And Diagnosis ◽  
Current Signatures

In this paper, a fault detection and diagnosis system for induction motors using motor current signature analysis and the Fuzzy Min-Max (FMM) neural network is described. The finite element method is first employed to generate experimental data for predicting the changes in stator current signatures of an induction motor due to broken rotor bars. Then, a series real laboratory experiments is for broken rotor bars detection and diagnosis. The induction motor with broken rotor bars is operated under different load conditions. In all the experiments, the FMM network is used to learn and distinguish between normal and faulty states of the induction motor based on the input features extracted from the power spectral density. The experimental results positively demonstrate that the FMM network is useful for fault detection and diagnosis of broken rotor bars in induction motors.

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