Cargando…

Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis

Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the re...

Descripción completa

Detalles Bibliográficos
Autores principales: Zamudio-Ramírez, Israel, Osornio-Ríos, Roque Alfredo, Antonino-Daviu, Jose Alfonso, Quijano-Lopez, Alfredo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085524/
https://www.ncbi.nlm.nih.gov/pubmed/32182665
http://dx.doi.org/10.3390/s20051477
_version_ 1783508951427448832
author Zamudio-Ramírez, Israel
Osornio-Ríos, Roque Alfredo
Antonino-Daviu, Jose Alfonso
Quijano-Lopez, Alfredo
author_facet Zamudio-Ramírez, Israel
Osornio-Ríos, Roque Alfredo
Antonino-Daviu, Jose Alfonso
Quijano-Lopez, Alfredo
author_sort Zamudio-Ramírez, Israel
collection PubMed
description Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the possibility of combining the information obtained from the analysis of different magnitudes should be explored, in order to achieve more reliable diagnostic conclusions, before the fault can develop into an irreversible damage. This paper proposes a smart-sensor that explores the weighted analysis of the axial, radial, and combination of both stray fluxes captured by a low-cost, easy setup, non-invasive, and compact triaxial stray flux sensor during the start-up transient through the short time Fourier transform (STFT) and characterizes specific patterns appearing on them using statistical parameters that feed a feature reduction linear discriminant analysis (LDA) and then a feed-forward neural network (FFNN) for classification purposes, opening the possibility of offering an on-site automatic fault diagnosis scheme. The obtained results show that the proposed smart-sensor is efficient for monitoring and diagnosing early induction motor electromechanical faults. This is validated with a laboratory induction motor test bench for individual and combined broken rotor bars and misalignment faults.
format Online
Article
Text
id pubmed-7085524
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-70855242020-03-23 Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis Zamudio-Ramírez, Israel Osornio-Ríos, Roque Alfredo Antonino-Daviu, Jose Alfonso Quijano-Lopez, Alfredo Sensors (Basel) Article Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the possibility of combining the information obtained from the analysis of different magnitudes should be explored, in order to achieve more reliable diagnostic conclusions, before the fault can develop into an irreversible damage. This paper proposes a smart-sensor that explores the weighted analysis of the axial, radial, and combination of both stray fluxes captured by a low-cost, easy setup, non-invasive, and compact triaxial stray flux sensor during the start-up transient through the short time Fourier transform (STFT) and characterizes specific patterns appearing on them using statistical parameters that feed a feature reduction linear discriminant analysis (LDA) and then a feed-forward neural network (FFNN) for classification purposes, opening the possibility of offering an on-site automatic fault diagnosis scheme. The obtained results show that the proposed smart-sensor is efficient for monitoring and diagnosing early induction motor electromechanical faults. This is validated with a laboratory induction motor test bench for individual and combined broken rotor bars and misalignment faults. MDPI 2020-03-08 /pmc/articles/PMC7085524/ /pubmed/32182665 http://dx.doi.org/10.3390/s20051477 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zamudio-Ramírez, Israel
Osornio-Ríos, Roque Alfredo
Antonino-Daviu, Jose Alfonso
Quijano-Lopez, Alfredo
Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis
title Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis
title_full Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis
title_fullStr Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis
title_full_unstemmed Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis
title_short Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis
title_sort smart-sensor for the automatic detection of electromechanical faults in induction motors based on the transient stray flux analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085524/
https://www.ncbi.nlm.nih.gov/pubmed/32182665
http://dx.doi.org/10.3390/s20051477
work_keys_str_mv AT zamudioramirezisrael smartsensorfortheautomaticdetectionofelectromechanicalfaultsininductionmotorsbasedonthetransientstrayfluxanalysis
AT osornioriosroquealfredo smartsensorfortheautomaticdetectionofelectromechanicalfaultsininductionmotorsbasedonthetransientstrayfluxanalysis
AT antoninodaviujosealfonso smartsensorfortheautomaticdetectionofelectromechanicalfaultsininductionmotorsbasedonthetransientstrayfluxanalysis
AT quijanolopezalfredo smartsensorfortheautomaticdetectionofelectromechanicalfaultsininductionmotorsbasedonthetransientstrayfluxanalysis