Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data

Accurate damping estimation is crucial for structural health monitoring and machinery diagnostics. This article introduces a novel wavelet-based framework for extracting the damping ratio from multiple impulse responses of vibrating systems. Extracting damping ratios is a numerically sensitive task, further complicated by the common assumption in the literature that impacts are perfectly aligned—a condition rarely met in practice. To address the challenge of non-synchronized recordings, we propose two wavelet-based algorithms that leverage wavelet energy for improved alignment and averaging in the wavelet domain to reduce noise, enhancing the robustness of damping estimation. Our approach provides a fresh perspective on the application of wavelets in damping estimation. We conduct a comprehensive evaluation, comparing the proposed methods with four traditional algorithms. The assessment is strengthened by incorporating both numerical simulations and experimental analysis. Additionally, we apply the analysis of variance (ANOVA) test to assess the significance of algorithm performance across varying numbers of recordings. The results highlight the sensitivity of damping estimation to time shifts, noise levels, and the number of recordings. The proposed wavelet-based approaches demonstrate outstanding adaptability and reliability, offering a promising solution for real-world applications.