An empirical time-domain trend line-based bridge signal decomposing algorithm using Savitzky-Golay filter

This paper develops a trend line-based algorithm for signal decomposition in which the adjusted Savitzky-Golay filter is utilized to initiate the decomposition process. In this line, the proposed algorithm determines some special trend lines, mainly composed of the natural frequency of a bridge. An easy-to-implement algorithm is then provided to formulate this process and to decompose the given signal into its components in a systematic way. Additionally, a residual signal is generated by the proposed algorithm to store the detected noise and to reconstruct the original signal. To verify the proposed algorithm in the field of bridge health monitoring, a set of numerical and experimental examples are offered in which the proposed algorithm is employed to decompose the signal and provide the constituent components. Moreover, the application of the proposed algorithm in damage localization of the bridge is addressed in the appendix using a simply supported bridge under a moving vehicle. Finally, the bridge example is solved by empirical mode decomposition, as a promising benchmark method, to further illustrate the accuracy of the results and compare them in detail.

Automated summary

This paper presents a trend line-based signal decomposition algorithm for bridge health monitoring, aiming to improve accuracy and effectiveness. The algorithm decomposes the input signal into components and generates a residual signal to store noise. By numerical and experimental examples, as well as a damage localization case study, the algorithm's effectiveness is validated.