Knowledge Discovery from Vibration Measurements
The framework as well as the particular algorithms of pattern recognition process is widely adopted in structural health monitoring (SHM). However, as a part of the overall process of knowledge discovery from data bases (KDD), the results of pattern recognition are only changes and patterns of chang...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/917524 |
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| author | Jun Deng Jian Li Daoyao Wang |
| author_facet | Jun Deng Jian Li Daoyao Wang |
| author_sort | Jun Deng |
| collection | DOAJ |
| description | The framework as well as the particular algorithms of pattern recognition process is widely adopted in structural health monitoring (SHM). However, as a part of the overall process of knowledge discovery from data bases (KDD), the results of pattern recognition are only changes and patterns of changes of data features. In this paper, based on the similarity between KDD and SHM and considering the particularity of SHM problems, a four-step framework of SHM is proposed which extends the final goal of SHM from detecting damages to extracting knowledge to facilitate decision making. The purposes and proper methods of each step of this framework are discussed. To demonstrate the proposed SHM framework, a specific SHM method which is composed by the second order structural parameter identification, statistical control chart analysis, and system reliability analysis is then presented. To examine the performance of this SHM method, real sensor data measured from a lab size steel bridge model structure are used. The developed four-step framework of SHM has the potential to clarify the process of SHM to facilitate the further development of SHM techniques. |
| format | Article |
| id | doaj-art-779b98b5125b41fd933a5aaf62550e08 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-779b98b5125b41fd933a5aaf62550e082025-02-03T05:46:17ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/917524917524Knowledge Discovery from Vibration MeasurementsJun Deng0Jian Li1Daoyao Wang2School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaGuangdong Provincial Academy of Building Research, Guangzhou 510500, ChinaSchool of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaThe framework as well as the particular algorithms of pattern recognition process is widely adopted in structural health monitoring (SHM). However, as a part of the overall process of knowledge discovery from data bases (KDD), the results of pattern recognition are only changes and patterns of changes of data features. In this paper, based on the similarity between KDD and SHM and considering the particularity of SHM problems, a four-step framework of SHM is proposed which extends the final goal of SHM from detecting damages to extracting knowledge to facilitate decision making. The purposes and proper methods of each step of this framework are discussed. To demonstrate the proposed SHM framework, a specific SHM method which is composed by the second order structural parameter identification, statistical control chart analysis, and system reliability analysis is then presented. To examine the performance of this SHM method, real sensor data measured from a lab size steel bridge model structure are used. The developed four-step framework of SHM has the potential to clarify the process of SHM to facilitate the further development of SHM techniques.http://dx.doi.org/10.1155/2014/917524 |
| spellingShingle | Jun Deng Jian Li Daoyao Wang Knowledge Discovery from Vibration Measurements The Scientific World Journal |
| title | Knowledge Discovery from Vibration Measurements |
| title_full | Knowledge Discovery from Vibration Measurements |
| title_fullStr | Knowledge Discovery from Vibration Measurements |
| title_full_unstemmed | Knowledge Discovery from Vibration Measurements |
| title_short | Knowledge Discovery from Vibration Measurements |
| title_sort | knowledge discovery from vibration measurements |
| url | http://dx.doi.org/10.1155/2014/917524 |
| work_keys_str_mv | AT jundeng knowledgediscoveryfromvibrationmeasurements AT jianli knowledgediscoveryfromvibrationmeasurements AT daoyaowang knowledgediscoveryfromvibrationmeasurements |