Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis
The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. Therefore, this work investigates two fault diagnosis solutions that exploit the direct estimation of the faults by mea...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/11/5035 |
| _version_ | 1797532112475127808 |
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| author | Saverio Farsoni Silvio Simani Paolo Castaldi |
| author_facet | Saverio Farsoni Silvio Simani Paolo Castaldi |
| author_sort | Saverio Farsoni |
| collection | DOAJ |
| description | The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. Therefore, this work investigates two fault diagnosis solutions that exploit the direct estimation of the faults by means of data-driven approaches. In this way, the diagnostic residuals are represented by the reconstructed faults affecting the monitored process. The proposed methodologies are based on fuzzy systems and neural networks used to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the considered prototypes are integrated with auto-regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. These residual generators are estimated from the input and output measurements acquired from a high-fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. The robustness and the reliability features of the developed solutions are validated in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. Moreover, a hardware-in-the-loop tool is implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches. The achieved results have demonstrated the effectiveness of the developed schemes also with respect to more complex model-based and data-driven fault diagnosis methodologies. |
| first_indexed | 2024-03-10T10:54:28Z |
| format | Article |
| id | doaj.art-3cce54b297de4eaf8aaa4d714ad2f2a8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:54:28Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3cce54b297de4eaf8aaa4d714ad2f2a82023-11-21T21:58:59ZengMDPI AGApplied Sciences2076-34172021-05-011111503510.3390/app11115035Fuzzy and Neural Network Approaches to Wind Turbine Fault DiagnosisSaverio Farsoni0Silvio Simani1Paolo Castaldi2Department of Engineering, University of Ferrara, 44122 Ferrara, ItalyDepartment of Engineering, University of Ferrara, 44122 Ferrara, ItalyDepartment of Electrical, Electronic, and Information Engineering, University of Bologna, 40136 Bologna, ItalyThe fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. Therefore, this work investigates two fault diagnosis solutions that exploit the direct estimation of the faults by means of data-driven approaches. In this way, the diagnostic residuals are represented by the reconstructed faults affecting the monitored process. The proposed methodologies are based on fuzzy systems and neural networks used to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the considered prototypes are integrated with auto-regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. These residual generators are estimated from the input and output measurements acquired from a high-fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. The robustness and the reliability features of the developed solutions are validated in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. Moreover, a hardware-in-the-loop tool is implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches. The achieved results have demonstrated the effectiveness of the developed schemes also with respect to more complex model-based and data-driven fault diagnosis methodologies.https://www.mdpi.com/2076-3417/11/11/5035fuzzy systemsneural networksfault diagnosisdata-driven approachesrobustness and reliabilitywind turbine |
| spellingShingle | Saverio Farsoni Silvio Simani Paolo Castaldi Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis Applied Sciences fuzzy systems neural networks fault diagnosis data-driven approaches robustness and reliability wind turbine |
| title | Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis |
| title_full | Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis |
| title_fullStr | Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis |
| title_full_unstemmed | Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis |
| title_short | Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis |
| title_sort | fuzzy and neural network approaches to wind turbine fault diagnosis |
| topic | fuzzy systems neural networks fault diagnosis data-driven approaches robustness and reliability wind turbine |
| url | https://www.mdpi.com/2076-3417/11/11/5035 |
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