Abstract
Smart wireless sensors have been recognized as a promising technology to overcome many inherent difficulties and limitations associated with traditional wired structural health monitoring (SHM) systems. Despite the advances in smart sensor technologies, on-board computing capability of smart sensors has been considered as one of the most difficult challenges in the application of the smart sensors in SHM. Taking the advantage of recent developments in microprocessor which provides powerful on-board computing functionality for smart sensors, this paper presents a new decentralized data processing approach for modal identification using the Hilbert-Huang transform (HHT) algorithm, which is based on signal decomposition technique. It is shown that this method is suitable for implementation in the intrinsically distributed computing environment found in wireless smart sensor networks (WSSNs). The HHT-based decentralized data processing is, then, programmed and implemented on the Crossbow IRIS mote sensor platform. The effectiveness of the proposed techniques is demonstrated through a set of numerical studies and experimental validations on an in-house cable-stayed bridge model in terms of the accuracy of identified dynamic properties.
Highlights
Vibration-based structural health monitoring (SHM) provides valuable information regarding the dynamic characteristics of structures
Taking the advantage of recent developments in microprocessor which provides powerful on-board computing functionality for smart sensors, this paper presents a new decentralized data processing approach for modal identification using the Hilbert-Huang transform (HHT) algorithm, which is based on signal decomposition technique
In order to realize data aggregation and modal identification based on HHT, this paper proposes a new distributed SHM scheme for the implementation in the intrinsically decentralized computing environment in wireless smart sensor networks (WSSNs)
Summary
Vibration-based structural health monitoring (SHM) provides valuable information regarding the dynamic characteristics of structures. The performance of data communication in wireless smart sensor networks (WSSNs) is based on the data acquisition and processing schemes within the WSSNs. A singlesensor node which generates 16-bit vibration data along three axes at 500 samples per second can consume onefourth of the nominal data rate of the IEEE 802.15.4 lowpower radio. In order to realize data aggregation and modal identification based on HHT, this paper proposes a new distributed SHM scheme for the implementation in the intrinsically decentralized computing environment in WSSNs. In this study, a laboratory bridge model is built and tested on a commercial off-the-shelf WSN platform. The identification of the modal properties for vibration signals using the embedded HHT method on smart sensors is examined The results from both simulation and experiments show that the proposed method achieves higher accuracy for identifying modal characteristics.
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