Name
Title | ||
|---|---|---|
Accurate diagnosis of induction machine faults using optimal time-frequency representations |
Abstract | ||
|---|---|---|
This paper presents a new diagnosis method of induction motor faults based on time-frequency classification of the current waveforms. This method is composed of two sequential processes: a feature extraction and a rule decision. In the process of feature extraction, the time-frequency representation (TFR) has been designed for maximizing the separability between classes representing different faults. The diagnosis is realised in two levels; the first one allows the detection of different faults-bearing fault, stator fault and rotor fault. The second one refines this detection by the determination of severity degree of faults, which are already identified on the previous level. The diagnosis is independent of the level of load. This method is validated on a 5.5kW induction motor test bench. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.engappai.2009.01.002 | Eng. Appl. of AI |
Keywords | Field | DocType |
time–frequency,new diagnosis method,feature extraction,stator fault,induction motor test bench,mahalanobis distance,accurate diagnosis,different fault,rotor fault,diagnosis,classification,induction motor,optimal time-frequency representation,previous level,different faults-bearing fault,induction motor fault,time-frequency classification,induction machine fault,time frequency,time frequency representation | Induction motor,Test bench,Pattern recognition,Computer science,Waveform,Mahalanobis distance,Feature extraction,Rotor (electric),Time–frequency analysis,Artificial intelligence,Stator,Machine learning | Journal |
Volume | Issue | ISSN |
22 | 4-5 | Engineering Applications of Artificial Intelligence |
Citations | PageRank | References |
6 | 0.53 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Lebaroud | 1 | 51 | 4.04 |
| G. Clerc | 2 | 36 | 3.46 |