| Full text | |
| Author(s): |
Silva, Luiz C.
;
Dias, Cleber G.
;
Alves, Wonder A. L.
;
Kurkova, V
;
Manolopoulos, Y
;
Hammer, B
;
Iliadis, L
;
Maglogiannis, I
Total Authors: 8
|
| Document type: | Journal article |
| Source: | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT I; v. 11139, p. 10-pg., 2018-01-01. |
| Abstract | |
The three-phase induction motors are widely used in a lot of applications both industry and other environments. Although this electrical machine is robust and reliable for industrial tasks, for example, conditioning monitoring techniques have been investigated during the last years to identify some electrical and mechanical faults in induction motors. In this sense, broken rotor bars is a typical fault related to the induction machine damage and the current technical solutions have shown some drawbacks for this kind of failure diagnosis, particularly when motor is running at very low slip. Therefore, this paper proposes a new use of Histogram of Oriented Gradients, usually applied in computer vision and image processing, for broken bars detection, using data from only one phase of the stator current of the machine. The intensity gradients and edge directions of each time-window of the stator signal have been applied as inputs for a neural network classifier. This method has been validated using some experimental data from a 7.5 kW squirrel cage induction machine running at distinct load levels (slip conditions). (AU) | |
| FAPESP's process: | 16/02525-1 - Diagnosis of broken bars in squirrel cage induction motors using intelligent techniques |
| Grantee: | Cleber Gustavo Dias |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 16/02547-5 - Shape analysis through the ultimate levelings |
| Grantee: | Wonder Alexandre Luz Alves |
| Support Opportunities: | Regular Research Grants |