Multi-Scale Principal Component Analysis for the Fault Detection and Isolation in Induction Motors

Naga Venkata Navya Repaka, Vidya Sagar Yellapu

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Article number18206
Pages (from-to)86-92
Number of pages7
JournalInternational Journal of Engineering and Technology(UAE)
Issue number3.31
Publication statusPublished - 24 Jan 2018
Externally publishedYes
EventInternational Conference on Applied Science and Technology - Manado, Indonesia
Duration: 24 Jan 201825 Jan 2018


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