Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window.

Jordi Burriel-Valencia,Ruben Puche-Panadero,Javier Martinez-Roman,Angel Sapena-Bano,Manuel Pineda-Sanchez

doi:10.3390/s18010146

Jordi Burriel-Valencia, Ruben Puche-Panadero + Show 3 more

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https://doi.org/10.3390/s18010146

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Abstract

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current’s spectrogram with a significant reduction of the required computational resources.

Highlights

Rotating electrical machines cover a broad range of applications in modern industrial installations.cage induction machines are the most widely used due to their robustness and low maintenance requirements
Fault diagnosis via the current analysis in the frequency domain has become a common method for machine condition evaluation because it is non-invasive, it requires a single current sensor, either a current transformer, a Hall sensor or a magnetoelectric current sensor [4], and it can identify a wide variety of machine faults [5,6]
transient MCSA (TMCSA) methods can extend the field of application of traditional motor current signature analysis (MCSA) methods to the fault diagnosis of electrical machines working in transient conditions, such as the start-up transient of an induction machine (IM), by replacing the FFT with the short time Fourier transform (STFT), which is able to display the signature of the fault components in the TF domain

Summary

Introduction

Rotating electrical machines cover a broad range of applications in modern industrial installations.cage induction machines are the most widely used due to their robustness and low maintenance requirements. Fault diagnosis via the current analysis in the frequency domain has become a common method for machine condition evaluation because it is non-invasive, it requires a single current sensor, either a current transformer, a Hall sensor or a magnetoelectric current sensor [4], and it can identify a wide variety of machine faults [5,6]. These techniques, known as motor current signature analysis (MCSA), have focused on the detection of faults during the steady state functioning of the machine through the current spectrum, which can be computed using the fast

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