Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals
Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance becaus...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/2/948 |
| _version_ | 1797443101413867520 |
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| author | Arturo Yosimar Jaen-Cuellar Miguel Trejo-Hernández Roque Alfredo Osornio-Rios Jose Alfonso Antonino-Daviu |
| author_facet | Arturo Yosimar Jaen-Cuellar Miguel Trejo-Hernández Roque Alfredo Osornio-Rios Jose Alfonso Antonino-Daviu |
| author_sort | Arturo Yosimar Jaen-Cuellar |
| collection | DOAJ |
| description | Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque–velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical features and genetic algorithms to find out those features that can best be used for detecting the gradual gear wear of a gearbox by using the signals, measured directly in the motor, from current and vibration sensors at different frequencies. The methodology also makes use of linear discriminant analysis to generate a bidimensional representation of the system conditions that are fed to a neural network with a simple structure for performing the classification of the condition. Four uniform gear wear conditions were tested, including the healthy state and three gradual conditions: 25%, 50%, and 75% wear in the gear teeth. Because of the sampling frequency, the number of sensors, the time for data acquisition, the different operation frequencies analyzed, and the computation of the different statistical features, meant that a large amount of data were generated that needed to be fused and reduced. Therefore, the proposed methodology provides an excellent generalized solution for data fusion and for minimizing the computational burden required. The obtained results demonstrate the effectiveness of fault gradualism detection for the proposed approach. |
| first_indexed | 2024-03-09T12:51:17Z |
| format | Article |
| id | doaj.art-c228e74ef4a1486e97f671f4a7f2fbbb |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T12:51:17Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c228e74ef4a1486e97f671f4a7f2fbbb2023-11-30T22:06:21ZengMDPI AGEnergies1996-10732023-01-0116294810.3390/en16020948Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration SignalsArturo Yosimar Jaen-Cuellar0Miguel Trejo-Hernández1Roque Alfredo Osornio-Rios2Jose Alfonso Antonino-Daviu3HSPdigital–CA Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Río Moctezuma 249, Col. San Cayetano, San Juan del Rio 76807, MexicoHSPdigital–CA Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Río Moctezuma 249, Col. San Cayetano, San Juan del Rio 76807, MexicoHSPdigital–CA Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Río Moctezuma 249, Col. San Cayetano, San Juan del Rio 76807, MexicoInstituto Tecnológico de la Energía, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, SpainKinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque–velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical features and genetic algorithms to find out those features that can best be used for detecting the gradual gear wear of a gearbox by using the signals, measured directly in the motor, from current and vibration sensors at different frequencies. The methodology also makes use of linear discriminant analysis to generate a bidimensional representation of the system conditions that are fed to a neural network with a simple structure for performing the classification of the condition. Four uniform gear wear conditions were tested, including the healthy state and three gradual conditions: 25%, 50%, and 75% wear in the gear teeth. Because of the sampling frequency, the number of sensors, the time for data acquisition, the different operation frequencies analyzed, and the computation of the different statistical features, meant that a large amount of data were generated that needed to be fused and reduced. Therefore, the proposed methodology provides an excellent generalized solution for data fusion and for minimizing the computational burden required. The obtained results demonstrate the effectiveness of fault gradualism detection for the proposed approach.https://www.mdpi.com/1996-1073/16/2/948electrical machinediagnosisfaults detectionindustrial motorsartificial intelligence |
| spellingShingle | Arturo Yosimar Jaen-Cuellar Miguel Trejo-Hernández Roque Alfredo Osornio-Rios Jose Alfonso Antonino-Daviu Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals Energies electrical machine diagnosis faults detection industrial motors artificial intelligence |
| title | Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals |
| title_full | Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals |
| title_fullStr | Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals |
| title_full_unstemmed | Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals |
| title_short | Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals |
| title_sort | gear wear detection based on statistic features and heuristic scheme by using data fusion of current and vibration signals |
| topic | electrical machine diagnosis faults detection industrial motors artificial intelligence |
| url | https://www.mdpi.com/1996-1073/16/2/948 |
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