A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic Identification
The shafting systems of hydropower units work as the core component for the conversion of water energy to electric energy and have been running for a long time in the hostile hydraulic–mechanical–electrical-coupled environment—their vibration faults are frequent. How to quickly and accurately identi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/11/4266 |
| _version_ | 1797491578082689024 |
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| author | Yousong Shi Jianzhong Zhou Jie Huang Yanhe Xu Baonan Liu |
| author_facet | Yousong Shi Jianzhong Zhou Jie Huang Yanhe Xu Baonan Liu |
| author_sort | Yousong Shi |
| collection | DOAJ |
| description | The shafting systems of hydropower units work as the core component for the conversion of water energy to electric energy and have been running for a long time in the hostile hydraulic–mechanical–electrical-coupled environment—their vibration faults are frequent. How to quickly and accurately identify vibration faults to improve the reliability of the unit is a key issue. This study proposes a novel shafting vibration fault identification framework, which is divided into three coordinated stages: nonlinear modeling, signal denoising, and holographic identification. A nonlinear dynamical model of bending–torsion coupling vibration induced by multiple excitation vibration sources of the shafting system is established in the first stage. The multi-stage signal denoising method combines Savitzky–Golay (SG) smoothing filtering, singular value decomposition (SVD), and variational mode decomposition (VMD). SG-SVD-VMD is used for the guide bearing the vibration signals in the second stage. Further, the holospectrum theory is innovatively introduced to obtain the holospectra of the simulated and measured signals, and the shafting vibration faults of the real unit are identified by comparing the holospectrum of the measured signal with the simulated signal. These results show that the shafting nonlinear model can effectively reflect the vibration characteristics of the coupled vibration source and reveal the influence and fault characteristics of each external excitation on the shafting vibration. The shafting vibration faults of operating units can be identified by analyzing the holospectra of the shafting simulation signals and measuring the noise reduction signals. Thus, this framework can guide the safe and stable operation of hydropower units. |
| first_indexed | 2024-03-10T00:51:18Z |
| format | Article |
| id | doaj.art-c64fa94d27aa4ca5adaa6da29f642f52 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:51:18Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c64fa94d27aa4ca5adaa6da29f642f522023-11-23T14:51:12ZengMDPI AGSensors1424-82202022-06-012211426610.3390/s22114266A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic IdentificationYousong Shi0Jianzhong Zhou1Jie Huang2Yanhe Xu3Baonan Liu4School of Civil and Hydraulic Engineering, Hua Zhong University of Science and Technology, Wuhan 430074, ChinaSchool of Civil and Hydraulic Engineering, Hua Zhong University of Science and Technology, Wuhan 430074, ChinaSchool of Civil and Hydraulic Engineering, Hua Zhong University of Science and Technology, Wuhan 430074, ChinaSchool of Civil and Hydraulic Engineering, Hua Zhong University of Science and Technology, Wuhan 430074, ChinaSchool of Civil and Hydraulic Engineering, Hua Zhong University of Science and Technology, Wuhan 430074, ChinaThe shafting systems of hydropower units work as the core component for the conversion of water energy to electric energy and have been running for a long time in the hostile hydraulic–mechanical–electrical-coupled environment—their vibration faults are frequent. How to quickly and accurately identify vibration faults to improve the reliability of the unit is a key issue. This study proposes a novel shafting vibration fault identification framework, which is divided into three coordinated stages: nonlinear modeling, signal denoising, and holographic identification. A nonlinear dynamical model of bending–torsion coupling vibration induced by multiple excitation vibration sources of the shafting system is established in the first stage. The multi-stage signal denoising method combines Savitzky–Golay (SG) smoothing filtering, singular value decomposition (SVD), and variational mode decomposition (VMD). SG-SVD-VMD is used for the guide bearing the vibration signals in the second stage. Further, the holospectrum theory is innovatively introduced to obtain the holospectra of the simulated and measured signals, and the shafting vibration faults of the real unit are identified by comparing the holospectrum of the measured signal with the simulated signal. These results show that the shafting nonlinear model can effectively reflect the vibration characteristics of the coupled vibration source and reveal the influence and fault characteristics of each external excitation on the shafting vibration. The shafting vibration faults of operating units can be identified by analyzing the holospectra of the shafting simulation signals and measuring the noise reduction signals. Thus, this framework can guide the safe and stable operation of hydropower units.https://www.mdpi.com/1424-8220/22/11/4266hydropower unitsshafting systemnonlinear dynamicsignal multistage denoisingholospectrumvibration fault identification |
| spellingShingle | Yousong Shi Jianzhong Zhou Jie Huang Yanhe Xu Baonan Liu A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic Identification Sensors hydropower units shafting system nonlinear dynamic signal multistage denoising holospectrum vibration fault identification |
| title | A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic Identification |
| title_full | A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic Identification |
| title_fullStr | A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic Identification |
| title_full_unstemmed | A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic Identification |
| title_short | A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic Identification |
| title_sort | vibration fault identification framework for shafting systems of hydropower units nonlinear modeling signal processing and holographic identification |
| topic | hydropower units shafting system nonlinear dynamic signal multistage denoising holospectrum vibration fault identification |
| url | https://www.mdpi.com/1424-8220/22/11/4266 |
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