Open AccessMulti-Scale Principal Component Analysis for the Fault Detection and Isolation in Induction Motors
Author(s) -
Naga Venkata Navya Repaka,
Vidya Sagar Yellapu
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.31.18206
Subject(s) - principal component analysis , stator , induction motor , fault detection and isolation , fault (geology) , wavelet , component (thermodynamics) , scale (ratio) , computer science , engineering , control theory (sociology) , control engineering , artificial intelligence , actuator , electrical engineering , physics , seismology , geology , control (management) , quantum mechanics , voltage , thermodynamics
Induction motors, though rugged, undergo faults due to wear and tear in their operation. Some faults have the characteristic property of influencing the stator current frequencies. Some side-band frequencies can be observed in the case of such faults. In this paper, a Multi-Scale Principal Component Analysis which combines wavelet analysis with principal component analysis has been applied to the data obtained from the simulation model of an induction motor. A 3-level decomposition of the data is performed and the principal component analysis is applied to high-frequency and low-frequency components of the data at various levels. The results suggest the use of the scheme for timely detection and identification of the faults which would endanger the motor from the otherwise possible destruction. It has also been proved that the scheme has the capability of detecting the sensor faults also, in addition to the motor faults.