Two-Step Approach to Processing Raw Strain Monitoring Data for Damage Detection of Structures under Operational Conditions
Strain data of structural health monitoring is a prospective to be made full use of, because it reflects the stress peak and fatigue, especially sensitive to local stress redistribution, which is the probably damage in the vicinity of the sensor. For decoupling structural damage and masking effects...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/20/6887 |
| _version_ | 1797513164935397376 |
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| author | Peng Ren Zhi Zhou |
| author_facet | Peng Ren Zhi Zhou |
| author_sort | Peng Ren |
| collection | DOAJ |
| description | Strain data of structural health monitoring is a prospective to be made full use of, because it reflects the stress peak and fatigue, especially sensitive to local stress redistribution, which is the probably damage in the vicinity of the sensor. For decoupling structural damage and masking effects caused by operational conditions to eliminate the adverse impacts on strain-based damage detection, small time-scale structural events, i.e., the short-term dynamic strain responses, are analyzed in this paper by employing unsupervised modeling. A two-step approach to successively processing the raw strain monitoring data in the sliding time window is presented, consisting of the wavelet-based initial feature extraction step and the decoupling step to draw damage indicators. The principal component analysis and a low-rank property-based subspace projection method are adopted as two alternative decoupling methodologies. The approach’s feasibility and robustness are substantiated by analyzing the strain monitoring data from a customized truss experiment to successfully remove the masking effects of operating loads and identify local damages even concerning accommodating situations of missing data and limited measuring points. This work also sheds light on the merit of a low-rank property to separate structural damages from masking effects by comparing the performances of the two optional decoupling methods of the distinct rationales. |
| first_indexed | 2024-03-10T06:13:48Z |
| format | Article |
| id | doaj.art-d9551f9d5e134a5a8d7f36e67a4ecd1d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:13:48Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d9551f9d5e134a5a8d7f36e67a4ecd1d2023-11-22T19:59:01ZengMDPI AGSensors1424-82202021-10-012120688710.3390/s21206887Two-Step Approach to Processing Raw Strain Monitoring Data for Damage Detection of Structures under Operational ConditionsPeng Ren0Zhi Zhou1School of Civil Engineering, Dalian University of Technology, Dalian 116081, ChinaSchool of Civil Engineering, Hainan University, Haikou 570228, ChinaStrain data of structural health monitoring is a prospective to be made full use of, because it reflects the stress peak and fatigue, especially sensitive to local stress redistribution, which is the probably damage in the vicinity of the sensor. For decoupling structural damage and masking effects caused by operational conditions to eliminate the adverse impacts on strain-based damage detection, small time-scale structural events, i.e., the short-term dynamic strain responses, are analyzed in this paper by employing unsupervised modeling. A two-step approach to successively processing the raw strain monitoring data in the sliding time window is presented, consisting of the wavelet-based initial feature extraction step and the decoupling step to draw damage indicators. The principal component analysis and a low-rank property-based subspace projection method are adopted as two alternative decoupling methodologies. The approach’s feasibility and robustness are substantiated by analyzing the strain monitoring data from a customized truss experiment to successfully remove the masking effects of operating loads and identify local damages even concerning accommodating situations of missing data and limited measuring points. This work also sheds light on the merit of a low-rank property to separate structural damages from masking effects by comparing the performances of the two optional decoupling methods of the distinct rationales.https://www.mdpi.com/1424-8220/21/20/6887structural health monitoringdamage detectionstrain sensordata interpretationmasking effectdecoupling |
| spellingShingle | Peng Ren Zhi Zhou Two-Step Approach to Processing Raw Strain Monitoring Data for Damage Detection of Structures under Operational Conditions Sensors structural health monitoring damage detection strain sensor data interpretation masking effect decoupling |
| title | Two-Step Approach to Processing Raw Strain Monitoring Data for Damage Detection of Structures under Operational Conditions |
| title_full | Two-Step Approach to Processing Raw Strain Monitoring Data for Damage Detection of Structures under Operational Conditions |
| title_fullStr | Two-Step Approach to Processing Raw Strain Monitoring Data for Damage Detection of Structures under Operational Conditions |
| title_full_unstemmed | Two-Step Approach to Processing Raw Strain Monitoring Data for Damage Detection of Structures under Operational Conditions |
| title_short | Two-Step Approach to Processing Raw Strain Monitoring Data for Damage Detection of Structures under Operational Conditions |
| title_sort | two step approach to processing raw strain monitoring data for damage detection of structures under operational conditions |
| topic | structural health monitoring damage detection strain sensor data interpretation masking effect decoupling |
| url | https://www.mdpi.com/1424-8220/21/20/6887 |
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