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Toward Compressed Sensing of Structural Monitoring Data Using Discrete Cosine Transform
Advancement in sensing devices such as wireless sensors and high-rate data acquisition systems have recently enhanced inherent ability of structural health monitoring (SHM) where a large amount of data could be acquired remotely and sent wirelessly from a multisensor network. However, the large amount of data collected from the structural systems is often associated with missing information, network jam, or packet loss while transmitting such large data. In this paper, discrete cosine transform (DCT) is explored as a potential data compression technique that can recover under-sampled vibration signals of structural systems, thereby reducing the overall burden of analyzing large-volume data in SHM. Apart from time-domain comparison, a novel time–frequency blind source separation is integrated with the DCT-based data compression technique to evaluate the accuracy of the proposed method in modal identification. The results of the proposed data compression technique are verified using a suite of numerical and experimental studies and compared with existing -norm minimization–based data compression method. The results show that the DCT could be considered as a powerful data compression tool for the vibration data containing damage signatures and low energy modes.
Toward Compressed Sensing of Structural Monitoring Data Using Discrete Cosine Transform
Advancement in sensing devices such as wireless sensors and high-rate data acquisition systems have recently enhanced inherent ability of structural health monitoring (SHM) where a large amount of data could be acquired remotely and sent wirelessly from a multisensor network. However, the large amount of data collected from the structural systems is often associated with missing information, network jam, or packet loss while transmitting such large data. In this paper, discrete cosine transform (DCT) is explored as a potential data compression technique that can recover under-sampled vibration signals of structural systems, thereby reducing the overall burden of analyzing large-volume data in SHM. Apart from time-domain comparison, a novel time–frequency blind source separation is integrated with the DCT-based data compression technique to evaluate the accuracy of the proposed method in modal identification. The results of the proposed data compression technique are verified using a suite of numerical and experimental studies and compared with existing -norm minimization–based data compression method. The results show that the DCT could be considered as a powerful data compression tool for the vibration data containing damage signatures and low energy modes.
Toward Compressed Sensing of Structural Monitoring Data Using Discrete Cosine Transform
Almasri, Nawaf (author) / Sadhu, Ayan (author) / Ray Chaudhuri, Samit (author)
2019-09-25
Article (Journal)
Electronic Resource
Unknown
| Taylor & Francis Verlag | 2023