MODEL-BASED FAULT DIAGNOSIS OF INDUCTION MOTORS USING NON-STATIONARY SIGNAL SEGMENTATION |
| |
| Affiliation: | 1. International Arctic Research Center, University of Alaska Fairbanks, Alaska, USA;2. Institute of Earth Sciences, Academia Sinica, Taipei, Taiwan;3. Space Science Institute, Macau University of Science and Technology, Macau;1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, PR China;2. School of Software, South China Normal University, Foshan 528225, PR China;1. Núcleo INTELYMEC-CIFICEN, Facultad de Ingeniería, Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina;2. Grupo GEA Universidad Nacional de Río Cuarto, Argentina;3. CONICET, Argentina |
| |
| Abstract: | Effective detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance and improved operational efficiency of induction motors running off the power supply mains. In this paper, an empirical model-based fault diagnosis system is developed for induction motors using recurrent dynamic neural networks and multiresolution signal processing methods. In addition to nameplate information required for the initial set-up, the proposed diagnosis system uses measured motor terminal currents and voltages, and motor speed. The effectiveness of the diagnosis system is demonstrated through staged motor faults of electrical and mechanical origin. The developed system is scalable to different power ratings and it has been successfully demonstrated with data from 2.2, 373 and 597 kW induction motors. Incremental tuning is used to adapt the diagnosis system during commissioning on an new motor, significantly reducing the system development time. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
