Decimation analysis in the signal processing of current to detect broken bars in induction machine

TL;DR

This paper investigates how decimation of current signals in induction motors can reduce computational load in fault diagnosis without losing critical information, enhancing embedded system performance.

Contribution

It introduces a decimation-based method for current signal processing that maintains fault detection accuracy while decreasing computational requirements.

Findings

Decimation significantly reduces FFT computation time.

Fault detection remains accurate after decimation.

Improved efficiency for embedded fault diagnosis systems.

Abstract

This paper presents a study on the reduction of the sampling frequency of the current signals of an induction motor, the reductions are performed by means time-decimation technique for digital signal processing. We have used the Fast Fourier Transform to obtain the fault signal spectrum of broken bars. The results have shown how the decimation technique significantly reduces the number of operations and the time required to calculate the Fast Fourier Transform without loss of information. This approach provides a better performance of embedded systems for fault diagnosis based on characteristic of amplitude signal modulation.