## 4 Experimental Validation  
In this section, we aim to validate the proposed algorithm using physical experiments. We run the same experiments as before but using a physical beam under dynamic loading. To this end it is important to build a signal acquisition system and integrate it with the algorithm. In other words we need to build both experimental hardware and software to validate the proposed algorithm in a \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **No Noise** & \begin{tabular}{c} **Gaussian White** \\ **Noise** \\ \end{tabular} & \
\begin{tabular}{c} **5\% Modal** \\ **Error** \\ \end{tabular} &
\begin{tabular}{c} **5\% Modal Error** \\ **and Gaussian White** \\ \end{tabular} \\ \hline Peak relative error method (PREM) & 0.38\% & 0.52\% & 6.07\% & 12.12\% \\ Angle cosine method (ACM) & 0.9741 & 0.9740 & 0.9703 & 0.9668 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation table of multi-frequency sinusoidal load identification results. Figure 7: The identification results of multi-frequency sinusoidal load. physical experiment. The hardware include the sensor and its connections and a signal acquisition module, a vibration exciter and a computer as well as the beam. The software is implemented in LabVIEW (National Instruments Ltd.) where beside the proposed algorithm we also use the modules: response acquisition and display, modal data reading, online identification of dynamic load and data storage and recall. The components of the experimental work are displayed in Figure 8.  
The algorithm implementation on LabVIEW (National Instruments Ltd.) is shown in Figure 9 and the source code is given in the Appendix A. After starting the system, the parameters are first initialized and then the software waits for operation command. Once the system is operational it initiates the mode data reading and Kalman Filter. Next, the data collection and the load identification begins. The response data collected, and real-time identified load is then displayed step-by-step on the user interface. Figure 8: The composition of online dynamic load identification system. Figure 9: Software running logic (**left**) and the parallel processing multitask architecture of software (**right**). To realize the software real-time load identification function, it is necessary to perform four different tasks simultaneously: uninterrupted response data acquisition, Kalman Filter iterative identification of the load, interface display and continuous force excitation. This puts a significant computational burden on the computing hardware. To deal with this it is important to develop a parallel implementation for the system. The programming language of LabVIEW, is inherently multi-threaded which helps to design a suitable implementation. We also show in Figure 10 the load identification interface which shows the needed parameters including mode truncation order, model node number, observation node number and excitation point position. Other parameters such as the natural frequencies and damping can be loaded from a saved file. Also, here we validate the implementation using the same two previous cases, namely multiple impacts at fixed intervals and constant frequency sinusoidal load. Figure 11 shows a schematic diagram for both experiments. In Figure 12 we show the experimental settings. The used exciter is JZT-2 while as an excitation source either the power amplifier (HEAS-50) or the force hammer (PCB 086C03) are used. The acceleration sensor (PCB 356A33) and acquisition board (NI USB 4431) are used to collect the response requested by the online dynamic load identification software developed to identify the load acting on the beam. It should be noted that the weight of the acceleration sensor is negligible relative to the weight of the beam. The experimental modal analysis is curried out using a force hammer to excite the beam at several points. Then the modal information such as natural frequency and modal damping are obtained automatically by the modal analyzer (M + P Smart Office).