Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS Accelerometer
Smartphone MEMS (Micro Electrical Mechanical System) accelerometers have relatively low sensitivity and high output noise density. Therefore, it cannot be directly used to track feeble vibrations such as structural vibrations. This article proposes an effective increase in the sensitivity of the sma...
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doaj-df6f83f3afd94dfbaa9278b973fecf352020-11-25T01:42:51ZengMDPI AGSensors1424-82202019-07-011914314310.3390/s19143143s19143143Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS AccelerometerAhmed Elhattab0Nasim Uddin1Eugene OBrien2Department of Civil, Construction, and Environmental Engineering, The University of Alabama at Birmingham, 1075 13th St S, Birmingham, AL 35205, USADepartment of Civil, Construction, and Environmental Engineering, The University of Alabama at Birmingham, 1075 13th St S, Birmingham, AL 35205, USASchool of Civil Engineering, University College Dublin, Newstead Block B, Belfield, Dublin D04V1W8, IrelandSmartphone MEMS (Micro Electrical Mechanical System) accelerometers have relatively low sensitivity and high output noise density. Therefore, it cannot be directly used to track feeble vibrations such as structural vibrations. This article proposes an effective increase in the sensitivity of the smartphone accelerometer utilizing the stochastic resonance (SR) phenomenon. SR is an approach where, counter-intuitively, feeble signals are amplified rather than overwhelmed by the addition of noise. This study introduces the 2D-frequency independent underdamped pinning stochastic resonance (2D-FI-UPSR) technique, which is a customized SR filter that enables identifying the frequencies of weak signals. To validate the feasibility of the proposed SR filter, an iPhone device is used to collect bridge acceleration data during normal traffic operation and the proposed 2D-FI-UPSR filter is used to process these data. The first four fundamental bridge frequencies are successfully identified from the iPhone data. In parallel to the iPhone, a highly sensitive wireless sensing network consists of 15 accelerometers (Silicon Designs accelerometers SDI-2012) is installed to validate the accuracy of the extracted frequencies. The measurement fidelity of the iPhone device is shown to be consistent with the wireless sensing network data with approximately 1% error in the first three bridge frequencies and 3% error in the fourth frequency.https://www.mdpi.com/1424-8220/19/14/3143stochastic resonancebridge inspectionstructural health monitoring, SHMbridge health monitoringfrequency independent stochastic resonanceSHM using smartphones |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ahmed Elhattab Nasim Uddin Eugene OBrien |
spellingShingle |
Ahmed Elhattab Nasim Uddin Eugene OBrien Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS Accelerometer Sensors stochastic resonance bridge inspection structural health monitoring, SHM bridge health monitoring frequency independent stochastic resonance SHM using smartphones |
author_facet |
Ahmed Elhattab Nasim Uddin Eugene OBrien |
author_sort |
Ahmed Elhattab |
title |
Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS Accelerometer |
title_short |
Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS Accelerometer |
title_full |
Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS Accelerometer |
title_fullStr |
Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS Accelerometer |
title_full_unstemmed |
Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS Accelerometer |
title_sort |
extraction of bridge fundamental frequencies utilizing a smartphone mems accelerometer |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-07-01 |
description |
Smartphone MEMS (Micro Electrical Mechanical System) accelerometers have relatively low sensitivity and high output noise density. Therefore, it cannot be directly used to track feeble vibrations such as structural vibrations. This article proposes an effective increase in the sensitivity of the smartphone accelerometer utilizing the stochastic resonance (SR) phenomenon. SR is an approach where, counter-intuitively, feeble signals are amplified rather than overwhelmed by the addition of noise. This study introduces the 2D-frequency independent underdamped pinning stochastic resonance (2D-FI-UPSR) technique, which is a customized SR filter that enables identifying the frequencies of weak signals. To validate the feasibility of the proposed SR filter, an iPhone device is used to collect bridge acceleration data during normal traffic operation and the proposed 2D-FI-UPSR filter is used to process these data. The first four fundamental bridge frequencies are successfully identified from the iPhone data. In parallel to the iPhone, a highly sensitive wireless sensing network consists of 15 accelerometers (Silicon Designs accelerometers SDI-2012) is installed to validate the accuracy of the extracted frequencies. The measurement fidelity of the iPhone device is shown to be consistent with the wireless sensing network data with approximately 1% error in the first three bridge frequencies and 3% error in the fourth frequency. |
topic |
stochastic resonance bridge inspection structural health monitoring, SHM bridge health monitoring frequency independent stochastic resonance SHM using smartphones |
url |
https://www.mdpi.com/1424-8220/19/14/3143 |
work_keys_str_mv |
AT ahmedelhattab extractionofbridgefundamentalfrequenciesutilizingasmartphonememsaccelerometer AT nasimuddin extractionofbridgefundamentalfrequenciesutilizingasmartphonememsaccelerometer AT eugeneobrien extractionofbridgefundamentalfrequenciesutilizingasmartphonememsaccelerometer |
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