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

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          Abstract

          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.

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          Most cited references40

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          Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques

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            Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

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              Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                08 March 2020
                March 2020
                : 20
                : 5
                : 1477
                Affiliations
                [1 ]Engineering Faculty, San Juan del Río Campus, Universidad Autónoma de Querétaro, Av. Río Moctezuma 249, San Juan del Río, Querétaro 76807, Mexico; isra.zam.ram@ 123456hotmail.com (I.Z.-R.); raosornio@ 123456hspdigital.org (R.A.O.-R.)
                [2 ]Instituto Tecnológico de la Energía, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain; aquijano@ 123456ite.upv.es
                Author notes
                [* ]Correspondence: joanda@ 123456die.upv.es ; Tel.: +34-96387-7592
                Author information
                https://orcid.org/0000-0003-0868-2918
                https://orcid.org/0000-0003-1898-2228
                Article
                sensors-20-01477
                10.3390/s20051477
                7085524
                32182665
                242c703b-f9cc-4acb-99ba-bb08b419fcbd
                © 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/).

                History
                : 09 February 2020
                : 05 March 2020
                Categories
                Article

                Biomedical engineering
                induction motor,smart-sensor,triaxial stray flux sensor,time–frequency transforms

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