Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition
This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature ex...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/248349 |
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| _version_ | 1832562621349363712 |
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| author | Wenjia Liu Bo Chen R. Andrew Swartz |
| author_facet | Wenjia Liu Bo Chen R. Andrew Swartz |
| author_sort | Wenjia Liu |
| collection | DOAJ |
| description | This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate. |
| format | Article |
| id | doaj-art-9409ee4d91a842b1bb7ce2abf7a6f526 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-9409ee4d91a842b1bb7ce2abf7a6f5262025-02-03T01:22:14ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/248349248349Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern RecognitionWenjia Liu0Bo Chen1R. Andrew Swartz2Elektrobit Automotive Inc. Detroit, Farmington Hills, MI 48331, USADepartment of Mechanical Engineering—Engineering Mechanics, Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI 49931, USADepartment of Civil and Environmental Engineering, Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI 49931, USAThis paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate.http://dx.doi.org/10.1155/2013/248349 |
| spellingShingle | Wenjia Liu Bo Chen R. Andrew Swartz Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition The Scientific World Journal |
| title | Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition |
| title_full | Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition |
| title_fullStr | Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition |
| title_full_unstemmed | Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition |
| title_short | Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition |
| title_sort | investigation of time series representations and similarity measures for structural damage pattern recognition |
| url | http://dx.doi.org/10.1155/2013/248349 |
| work_keys_str_mv | AT wenjialiu investigationoftimeseriesrepresentationsandsimilaritymeasuresforstructuraldamagepatternrecognition AT bochen investigationoftimeseriesrepresentationsandsimilaritymeasuresforstructuraldamagepatternrecognition AT randrewswartz investigationoftimeseriesrepresentationsandsimilaritymeasuresforstructuraldamagepatternrecognition |