An unsupervised, on-line system for induction motor fault detection using stator current monitoring

1995 ◽  
Vol 31 (6) ◽  
pp. 1280-1286 ◽  
Author(s):  
R.R. Schoen ◽  
B.K. Lin ◽  
T.G. Habetler ◽  
J.H. Schlag ◽  
S. Farag
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Line System ◽  
Stator Current ◽  
On Line ◽  
Current Monitoring

Fault Detection of Automobile Transmission Gears Using General Parameter Methods

2000 ◽  
Vol 12 (6) ◽  
pp. 702-705 ◽  
Author(s):  
Yasuhiko Dote ◽  
◽  
Seppo J. Ovaska ◽  
Xiao-Zhi Gao ◽  
Keyword(s):  
Fault Detection ◽  
Radial Basis Function Network ◽  
Fault Detection And Diagnosis ◽  
Fine Tuning ◽  
Detection Sensitivity ◽  
Inverse Filtering ◽  
Detection Scheme ◽  
Line System ◽  
On Line ◽  
General Parameter

This paper compares the performance of nonlinear Radial Basis Function Network-based (RBFN) and linear AutoRegressive (AR) model-based General Parameter (GP) methods in a fault detection application. We use the efficient GP approach for initializing the weights of the RBFN model in the beginning of the off-line system identification phase, as well as for fine-tuning the modeling accuracy of RBFN and AR models on-line. Our fault detection scheme is based on monitoring the expectation value of the scalar general parameter. This provides improved robustness and detection sensitivity over such methods where the on-line prediction error is used directly in the decision making process. In order to illustrate the performance of the proposed nonlinear and linear schemes, they are applied to fault detection of automobile transmission gears. As the acoustic sound level time-series, providing the necessary basis information for fault detection, is slightly nonlinear, the GPRBFN outperformed the linear methods: the GP-AR method and conventional AR inverse filtering. Both of the GP-based methods provide competitive solutions for real-world fault detection and diagnosis applications.

On-Line System for Fault Detection in Induction Machines based on Wavelet Convolution

2007 ◽  
Author(s):  
J. Cusido ◽  
J.A. Rosero ◽  
M. Cusido ◽  
A. Garcia ◽  
J.A. Ortega ◽  
...  
Keyword(s):  
Fault Detection ◽  
Induction Machines ◽  
Line System ◽  
On Line

Induction Motor Fault Detection and Diagnosis Using Artifical Neural Networks

2005 ◽  
Author(s):  
Chris K. Mechefske ◽  
Lingxin Li
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Induction Motor ◽  
Mechanical Vibration ◽  
Fault Detection And Diagnosis ◽  
Self Organizing Maps ◽  
Artifical Neural Networks ◽  
On Line ◽  
Motor Condition ◽  
Detection And Diagnosis

This paper investigates induction motor fault detection and diagnosis using Artificial Neural Networks (ANN). The ANN techniques include feedforward backpropagation networks (FFBPN) and self organizing maps (SOM), used individually and in combination. Common induction motor faults such as bearing faults, stator winding fault, unbalanced rotor and broken rotor bars are considered. The ANNs were trained and tested using dynamic measurements of stator currents and mechanical vibration signals. The effects of different network structures and the training set sizes on the performance of the ANNs are discussed. This study shows that, while the feedforward ANNs give satisfactory results and the SOMs can classify the type of motor fault during steady state working conditions, using a combination of SOM and FFBPN techniques yields superior fault detection and diagnostic accuracy. In addition, incipient motor fault detection has been investigated. The above results show that improved induction motor maintenance strategies may be possible through the use of comprehensive on-line induction motor condition monitoring and fault diagnosis systems.

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