Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum
Establishing a prediction model is a key step for the implementation of prognostic and health management. The prediction model can be used to forecast the change trend of the characteristics of the vibration signal and analyze the potential failure in the future. Taking the vibration of power plant...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2017/6103947 |
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| _version_ | 1850213579732549632 |
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| author | Lei Chen Jie Han Wenping Lei ZhenHong Guan Yajuan Gao |
| author_facet | Lei Chen Jie Han Wenping Lei ZhenHong Guan Yajuan Gao |
| author_sort | Lei Chen |
| collection | DOAJ |
| description | Establishing a prediction model is a key step for the implementation of prognostic and health management. The prediction model can be used to forecast the change trend of the characteristics of the vibration signal and analyze the potential failure in the future. Taking the vibration of power plant steam turbine as an example, the full vector fusion and fault prediction were studied. Due to the fact that the evaluation of the machine fault with only one transducer may result in a fault judgement with partiality, an information fusion method based on the theory of full vector spectrum was adopted to extract the vibration feature. An autoregressive prediction model was established. The collected vibration signals with pairing channels were fused. The time sequence of the fused vectors and spectrums were used to build the prediction model. The amplitude of main vector of rotating frequency and spectrum order structure were analyzed and predicted. The uncertainty of the spectrum structure can be eliminated by the information fusion. The reliability of the fault prediction was improved. The study on vibration prediction model system laid a technical foundation for the fault prognostic research. |
| format | Article |
| id | doaj-art-83bbcfbfc0e6470d83e227197342b3dc |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-83bbcfbfc0e6470d83e227197342b3dc2025-08-20T02:09:07ZengWileyShock and Vibration1070-96221875-92032017-01-01201710.1155/2017/61039476103947Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector SpectrumLei Chen0Jie Han1Wenping Lei2ZhenHong Guan3Yajuan Gao4Institute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, ChinaInstitute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, ChinaInstitute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, ChinaInstitute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, ChinaInstitute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, ChinaEstablishing a prediction model is a key step for the implementation of prognostic and health management. The prediction model can be used to forecast the change trend of the characteristics of the vibration signal and analyze the potential failure in the future. Taking the vibration of power plant steam turbine as an example, the full vector fusion and fault prediction were studied. Due to the fact that the evaluation of the machine fault with only one transducer may result in a fault judgement with partiality, an information fusion method based on the theory of full vector spectrum was adopted to extract the vibration feature. An autoregressive prediction model was established. The collected vibration signals with pairing channels were fused. The time sequence of the fused vectors and spectrums were used to build the prediction model. The amplitude of main vector of rotating frequency and spectrum order structure were analyzed and predicted. The uncertainty of the spectrum structure can be eliminated by the information fusion. The reliability of the fault prediction was improved. The study on vibration prediction model system laid a technical foundation for the fault prognostic research.http://dx.doi.org/10.1155/2017/6103947 |
| spellingShingle | Lei Chen Jie Han Wenping Lei ZhenHong Guan Yajuan Gao Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum Shock and Vibration |
| title | Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum |
| title_full | Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum |
| title_fullStr | Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum |
| title_full_unstemmed | Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum |
| title_short | Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum |
| title_sort | prediction model of vibration feature for equipment maintenance based on full vector spectrum |
| url | http://dx.doi.org/10.1155/2017/6103947 |
| work_keys_str_mv | AT leichen predictionmodelofvibrationfeatureforequipmentmaintenancebasedonfullvectorspectrum AT jiehan predictionmodelofvibrationfeatureforequipmentmaintenancebasedonfullvectorspectrum AT wenpinglei predictionmodelofvibrationfeatureforequipmentmaintenancebasedonfullvectorspectrum AT zhenhongguan predictionmodelofvibrationfeatureforequipmentmaintenancebasedonfullvectorspectrum AT yajuangao predictionmodelofvibrationfeatureforequipmentmaintenancebasedonfullvectorspectrum |