Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction
Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault charac...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | International Journal of Rotating Machinery |
| Online Access: | http://dx.doi.org/10.1155/2016/5980802 |
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| _version_ | 1832558195779829760 |
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| author | Lei Chen Jie Han Wenping Lei Yongxiang Cui Zhenhong Guan |
| author_facet | Lei Chen Jie Han Wenping Lei Yongxiang Cui Zhenhong Guan |
| author_sort | Lei Chen |
| collection | DOAJ |
| description | Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full-vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithm were analyzed. At last, the fault prediction method based on full-vector spectrum was proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full-vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters. |
| format | Article |
| id | doaj-art-b32e1b307a364ee6ba05a27af4cdf074 |
| institution | Kabale University |
| issn | 1023-621X 1542-3034 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Rotating Machinery |
| spelling | doaj-art-b32e1b307a364ee6ba05a27af4cdf0742025-02-03T01:32:57ZengWileyInternational Journal of Rotating Machinery1023-621X1542-30342016-01-01201610.1155/2016/59808025980802Full-Vector Signal Acquisition and Information Fusion for the Fault PredictionLei Chen0Jie Han1Wenping Lei2Yongxiang Cui3Zhenhong Guan4Institute 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, ChinaFault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full-vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithm were analyzed. At last, the fault prediction method based on full-vector spectrum was proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full-vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters.http://dx.doi.org/10.1155/2016/5980802 |
| spellingShingle | Lei Chen Jie Han Wenping Lei Yongxiang Cui Zhenhong Guan Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction International Journal of Rotating Machinery |
| title | Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction |
| title_full | Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction |
| title_fullStr | Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction |
| title_full_unstemmed | Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction |
| title_short | Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction |
| title_sort | full vector signal acquisition and information fusion for the fault prediction |
| url | http://dx.doi.org/10.1155/2016/5980802 |
| work_keys_str_mv | AT leichen fullvectorsignalacquisitionandinformationfusionforthefaultprediction AT jiehan fullvectorsignalacquisitionandinformationfusionforthefaultprediction AT wenpinglei fullvectorsignalacquisitionandinformationfusionforthefaultprediction AT yongxiangcui fullvectorsignalacquisitionandinformationfusionforthefaultprediction AT zhenhongguan fullvectorsignalacquisitionandinformationfusionforthefaultprediction |