Spur gearbox mixed fault detection using vibration envelope and motor stator current signatures analysis

2016 ◽  
Author(s):  
Walid Touti ◽  
Mohamed Salah ◽  
Samira Ben Salem ◽  
Khmais Bacha ◽  
Abdelkader Chaari
Keyword(s):  
Fault Detection ◽  
Stator Current ◽  
Current Signatures ◽  
Spur Gearbox

Planetary Gear Fault Detection Based on Mechanical Torque and Stator Current Signatures of a Wound Rotor Induction Generator

2018 ◽  
Vol 33 (3) ◽  
pp. 1072-1085 ◽  
Author(s):  
Mohammad Hoseintabar Marzebali ◽  
Jawad Faiz ◽  
Gerard-Andre Capolino ◽  
Shahin Hedayati Kia ◽  
Humberto Henao
Keyword(s):  
Fault Detection ◽  
Planetary Gear ◽  
Induction Generator ◽  
Stator Current ◽  
Gear Fault ◽  
Current Signatures

Fault Detection of Electric Motors Application Using ML Estimation Method

2012 ◽  
Vol 591-593 ◽  
pp. 1958-1961
Author(s):  
Juggrapong Treetrong
Keyword(s):  
Fault Detection ◽  
Estimation Method ◽  
Load Condition ◽  
Fault Analysis ◽  
Electric Motors ◽  
Ml Estimation ◽  
Stator Current ◽  
Fault Severity ◽  
Severity Levels ◽  
Stator Fault

This paper proposes a new method of motor fault detection. ML Estimation is proposed as a key technique for signal processing. The stator current is used data for motor fault analysis. ML Estimation is generally applied to estimate signals for nonlinear model. The expectation is that the method can provide information for fault analysis. The method is tested on 3 different motor conditions: healthy, stator fault, and rotor fault motor at full load condition. Based on experiments, the method can differentiate conditions clearly and be also able to measure fault severity levels.

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.

Sign in / Sign up

Export Citation Format

Share Document