# Development and Field Validation of Wireless Sensors for Railway Bridge Modal Identification
## 4 Wireless Sensor Node Measurement Performance Assessment
Figure 11: Averaged normalized power spectral densities for the lateral axis (**top**) and vertical axis (**bottom**) computed from acceleration time histories acquired by wireless sensors. Red circle represents natural frequencies identified through the peak-picking technique. As can be observed from Table 2, experimental values obtained from wireless sensors are in good agreement with the reference ones obtained through the processing of signals coming from the conventional system. The fact that the frequencies related to both vertical and lateral second mode have not been detected is due to the positioning of the only two installed prototypes, namely the midspan, which represents a nodal point for second modes (Figure 4). This comparison allows the validation of the developed solution for railway bridge modal identification purposes. As already explained, the main goal of this data analysis was the identification of the bridge natural frequencies to assess the validity of data acquired by sensor nodes through a comparison with reference ones. Once identified the natural frequencies, their values can also be monitored in time. In fact, one consequence of the presence of a structural damage is a drop in the bridge natural frequencies. An increase in the damage severity leads to a higher drop in the natural frequency [42]. Anyway, a slight decrease in the values can be associated to the variation in environmental conditions (i.e., temperature variation, etc.). Therefore, usually a damage can be detected when a variation of frequencies larger than 5% is observed [43]. In order to understand if the natural frequencies computed through the data coming from wireless sensors present a too high scatter to be potentially adopted as diagnostic parameters, their variation in time is considered. In particular, the trend in time of first lateral frequency values estimated by sensor nodes over an 18-day time window is considered in Figure 12. As can be noticed, the variation of this parameter does not present a clear decreasing trend, and it is anyway well below a \(\pm\)5% variation with respect to the starting detected frequency value. The results obtained allow the assessment of the measurement performance of the developed sensor nodes for modal identification and continuous monitoring purposes. Since the variation in time of natural frequency values represents one of the simplest SHM technique to identify the possible presence of structural issues, the FFT computation could be implemented directly on on-board sensors to send a synthetic and significant indicator of the bridge health. This on-board operation could avoid the transmission and post processing of time histories, limiting the data amount to be treated. More complex SHM algorithms will be studied in the future, and the possibility of implementing them at the gateway or sensor node level will be analyzed. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Vibration Mode** & **Wired System Estimation** & **Wireless System Estimation** \\ \hline
1 st Vertical (bending) & 2.5 Hz & 2.64 Hz \\
1 st Lateral & 3.5 Hz & 3.52 Hz \\
1 st Vertical (torsional) & 3.5 Hz & 3.52 Hz \\
2 nd Vertical (bending) & 5.8 Hz & - \\
2 nd Lateral & 6.7 Hz & - \\
3 rd Vertical (bending) & 8.5 Hz & 8.11 Hz \\
2 nd Vertical (torsional) & 9.5 Hz & - \\
3 rd Lateral & 9.7 Hz & 9.68 Hz \\
3 rd Vertical (torsional) & 13.1 Hz & 12.99 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between natural frequencies estimated by the wired and wireless monitoring systems.