Comparative study of advanced techniques for the diagnosis of induction motors

Abderrahmane Khechekhouche,Zied Driss,Abderrrahim Allal

doi:10.37868/hsd.v3i1.49

Abderrahmane Khechekhouche, Zied Driss + Show 1 more

Open Access

https://doi.org/10.37868/hsd.v3i1.49

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Abstract

This work is a comparative study between the various advanced technologies of diagnosis of induction motors published recently and to make a classification of these diagnostic techniques according to their sensitivities from experimental results of stator short-circuit faults between stator turns. By using the logarithmic FFT spectrum, we can discover the best method to detect faults in their early stages so that we can predict their faults and anticipate breakdowns that can be dangerous for people or the economy.

Highlights

The qualities of asynchronous electric motors are that they can sometimes present to the stator and the rotor different types of defects causing premature ageing
Another very advanced technique called "Hilbert Park vector product approach" (HPVPA) which was inspired by their previous technique
We will carry out a study in order to make a comparison between the various advanced technologies and to make a classification of these diagnostic techniques according to their sensitivities using experimental results of stator short-circuit faults between stator turns

Summary

Introduction

The qualities of asynchronous electric motors are that they can sometimes present to the stator and the rotor different types of defects causing premature ageing. Several methods for diagnosing faults in induction motors are published and proposed by researchers in journals supervised by diagnostic laboratories. They proposed the classical method based on the signature analysis of the motor current of induction motors (MCSA) which is an online diagnostic system with various advanced signal processing algorithms. Another study proposed a more advanced signal processing method based on the Park-Hilbert "Park-Hilbert" transformation (PVSMP-H). This group of researchers used "Park vector square modulus" (PVSM) and line current to obtain "motor square current signature analysis" (MSCSA) [8,9,10,11]. Using the logarithmic FFT spectrum, we can discover the best method to detect faults in their early stages to be able to predict their faults and

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