6533b7d7fe1ef96bd126830f
RESEARCH PRODUCT
Parameter Identification of a Winding Function Based Model for Fault Detection of Induction Machines
Surya Teja KandukuriWitold PawlusHuynh Van KhangKjell G. Robbersmyrsubject
Set (abstract data type)Identification (information)Bar (music)Control theorySimple (abstract algebra)Computer scienceWork (physics)Fault (power engineering)Induction motorFault detection and isolationdescription
Prediction of machines' faulty parts is important in industrial applications in order to reduce productivity losses. As far as electrical machines are considered, a model-based fault diagnosis approach is usually used for this purpose. The model is derived from the modified winding function theory and hence, it requires a considerable amount of parameters at various operating conditions in order to be successfully used. However, the complete set of parameters is difficult to be obtained, as manufacturers of electric machines normally provide only the parameters that describe simple motor models (e.g. T-equivalent circuit at rated conditions). Therefore, the current work presents a method that can be used to estimate more detailed motor parameters. In addition, these parameters are then used in an expanded induction motor model which, in turn, is applied to study severity of a broken bar fault in an induction machine.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-06-01 | 2018 Eighth International Conference on Information Science and Technology (ICIST) |