The Bulletin of the Polish Academy of Sciences: Technical Sciences is published bimonthly by the Division IV Engineering Sciences of the Polish Academy of Sciences, since the beginning of the existence of the PAS in 1952. The journal is peer‐reviewed and is published both in printed and electronic form. It is established for the publication of original high quality papers from multidisciplinary Engineering sciences with the following topics preferred:
Artificial and Computational Intelligence,
Biomedical Engineering and Biotechnology,
Control, Informatics and Robotics,
Electronics, Telecommunication and Optoelectronics,
Mechanical and Aeronautical Engineering, Thermodynamics,
Bulletin of the Polish Academy of Sciences: Technical Sciences jest czasopismem wydawanym w wolnym dostępie na licencji CC BY-NC-ND 4.0.
Bulletin of the Polish Academy of Sciences: Technical Sciences is an open access journal with all content available with no charge in full text version. The journal content is available under the licencse CC BY-NC-ND 4.0.
Abstract This paper deals with the application of the Radial Basis Function (RBF) networks for the induction motor fault detection. The rotor faults are analysed and fault symptoms are described. Next the main stages of the design methodology of the RBF-based neural detectors are described. These networks are trained and tested using measurement data of the stator current (MCSA). The efficiency of developed RBF-NN detectors is evaluated. Furthermore, influence of neural networks complexity and parameters of the RBF activation function on the quality of data classification is shown. The presented neural detectors are tested with measurement data obtained in the laboratory setup containing the converter-fed induction motor (IM) and changeable rotors with a different degree of damages