Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals
Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/1/316 |
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| author | Vicente Biot-Monterde Angela Navarro-Navarro Israel Zamudio-Ramirez Jose A. Antonino-Daviu Roque A. Osornio-Rios |
| author_facet | Vicente Biot-Monterde Angela Navarro-Navarro Israel Zamudio-Ramirez Jose A. Antonino-Daviu Roque A. Osornio-Rios |
| author_sort | Vicente Biot-Monterde |
| collection | DOAJ |
| description | Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to the high currents flowing along those bars during start-up. In order to reduce the stresses that could lead to the appearance of these faults, the use of soft starters is becoming usual. However, these devices introduce additional components in the current and flux signals, affecting the evolution of the fault-related patterns and so making the fault diagnosis process more difficult. This paper proposes a new method to automatically classify the rotor health state in IMs driven by soft starters. The proposed method relies on obtaining the Persistence Spectrum (PS) of the start-up stray-flux signals. To obtain a proper dataset, Data Augmentation Techniques (DAT) are applied, adding Gaussian noise to the original signals. Then, these PS images are used to train a Convolutional Neural Network (CNN), in order to automatically classify the rotor health state, depending on the severity of the fault, namely: healthy motor, one broken bar and two broken bars. This method has been validated by means of a test bench consisting of a 1.1 kW IM driven by four different soft starters coupled to a DC motor. The results confirm the reliability of the proposed method, obtaining a classification rate of 100.00% when analyzing each model separately and 99.89% when all the models are analyzed at a time. |
| first_indexed | 2024-03-09T09:41:03Z |
| format | Article |
| id | doaj.art-bd476f2084ad40488547278d7bd90c75 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:41:03Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bd476f2084ad40488547278d7bd90c752023-12-02T00:55:30ZengMDPI AGSensors1424-82202022-12-0123131610.3390/s23010316Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux SignalsVicente Biot-Monterde0Angela Navarro-Navarro1Israel Zamudio-Ramirez2Jose A. Antonino-Daviu3Roque A. Osornio-Rios4Instituto Tecnológico de la Energía (ITE), Universitat Politècnica de València (UPV), Camino de Vera S/N, 46022 Valencia, SpainInstituto Tecnológico de la Energía (ITE), Universitat Politècnica de València (UPV), Camino de Vera S/N, 46022 Valencia, SpainInstituto Tecnológico de la Energía (ITE), Universitat Politècnica de València (UPV), Camino de Vera S/N, 46022 Valencia, SpainInstituto Tecnológico de la Energía (ITE), Universitat Politècnica de València (UPV), Camino de Vera S/N, 46022 Valencia, SpainHSPdigital CA-Mecatronica Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76806, MexicoDue to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to the high currents flowing along those bars during start-up. In order to reduce the stresses that could lead to the appearance of these faults, the use of soft starters is becoming usual. However, these devices introduce additional components in the current and flux signals, affecting the evolution of the fault-related patterns and so making the fault diagnosis process more difficult. This paper proposes a new method to automatically classify the rotor health state in IMs driven by soft starters. The proposed method relies on obtaining the Persistence Spectrum (PS) of the start-up stray-flux signals. To obtain a proper dataset, Data Augmentation Techniques (DAT) are applied, adding Gaussian noise to the original signals. Then, these PS images are used to train a Convolutional Neural Network (CNN), in order to automatically classify the rotor health state, depending on the severity of the fault, namely: healthy motor, one broken bar and two broken bars. This method has been validated by means of a test bench consisting of a 1.1 kW IM driven by four different soft starters coupled to a DC motor. The results confirm the reliability of the proposed method, obtaining a classification rate of 100.00% when analyzing each model separately and 99.89% when all the models are analyzed at a time.https://www.mdpi.com/1424-8220/23/1/316induction motorCNNstray-fluxautomatic fault diagnosissoft startersbroken rotor bars |
| spellingShingle | Vicente Biot-Monterde Angela Navarro-Navarro Israel Zamudio-Ramirez Jose A. Antonino-Daviu Roque A. Osornio-Rios Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals Sensors induction motor CNN stray-flux automatic fault diagnosis soft starters broken rotor bars |
| title | Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals |
| title_full | Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals |
| title_fullStr | Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals |
| title_full_unstemmed | Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals |
| title_short | Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals |
| title_sort | automatic classification of rotor faults in soft started induction motors based on persistence spectrum and convolutional neural network applied to stray flux signals |
| topic | induction motor CNN stray-flux automatic fault diagnosis soft starters broken rotor bars |
| url | https://www.mdpi.com/1424-8220/23/1/316 |
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