Abstract
The use of digital accelerometers featuring high sensitivity and low noise levels in wireless smart sensors (WSSs) is becoming increasingly common for structural health monitoring (SHM) applications. Improvements in the design of Micro Electro-Mechanical System (MEMS) based digital accelerometers allow for high resolution sensing required for SHM with low power consumption suitable for WSSs. However, new approaches are needed to synchronize data from these sensors. Data synchronization is essential in wireless smart sensor networks (WSSNs) for accurate condition assessment of structures and reduced false-positive indications of damage. Efforts to achieve synchronized data sampling from multiple WSS nodes with digital accelerometers have been lacking, primarily because these sensors feature an internal Analog to Digital Converter (ADC) to which the host platform has no direct access. The result is increased uncertainty in the ADC startup time and thus worse synchronization among sensors. In this study, a high-sensitivity digital accelerometer is integrated with a next-generation WSS platform, the Xnode. An adaptive iterative algorithm is used to characterize these delays without the need for a dedicated evaluation setup and hardware-level access to the ADC. Extensive tests are conducted to evaluate the performance of the accelerometer experimentally. Overall time-synchronization achieved is under 15 µs, demonstrating the efficacy of this approach for synchronization of critical SHM applications.
Highlights
Structural health monitoring (SHM) involves the assessment of civil infrastructure for operational safety, integrity, and remaining useful life estimation
We provide a brief overview of time synchronization strategies in wireless smart sensor networks (WSSNs) for structural health monitoring (SHM); additional challenges encountered in data synchronization by incorporating digital accelerometers are discussed in detail, and an algorithm is proposed to characterize these uncertainties in wireless smart sensors (WSSs)
), which is estimated in μs using error slope of the phase angle obtained from the cross power spectral density (CPSD) between the pair of sensors is indicative of time synchronization θphase error6 (TSerror ), which is estimated in μ s using phase where θphase is the slope of the straight-line fit on the phase angle curve
Summary
Structural health monitoring (SHM) involves the assessment of civil infrastructure for operational safety, integrity, and remaining useful life estimation. Even clock synchronization in SHM applications faces some nontraditional challenges, which includes data acquisition at high-sampling rates, long-duration continuous sensing, nonlinear clock drifts arising from temperature variations among WSSs, and demand for rapid wake-up-response to sudden events. These challenges are discussed in detail for analog sensors in work by Li et al [10]. This approach can be generalized and extended to many of the available digital sensors.
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