A novel online identification approach for distributed dynamic load based on signal separation and improved Kalman filter algorithm

• Published in: Journal of sound and vibration Vol. 618; p. 119308
• Main Authors: Tang, Hongzhi, Jiang, Jinhui, Zhang, Fang, Kan, Lei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 10.12.2025
• Subjects: Adaptive algorithm; Distributed dynamic load identification; Kalman filter; Principal component analysis; Sparse regularization; Sparse regularization; Adaptive algorithm; Distributed dynamic load identification; Kalman filter; Principal component analysis
• ISSN: 0022-460X
• DOI: 10.1016/j.jsv.2025.119308

•A novel method identifies time–space coupled distributed dynamic loads.•Distributed dynamic loads are modeled within a state-space framework.•Necessary conditions and computational complexity are discussed in detail.•The method is robust to noise and applies to beam and plate structures. Distributed dynamic loads are commonly encountered in engineering applications. The identification of such loads, especially time-space coupled distributed dynamic loads, is an emerging area of research. Accurately representing these loads requires capturing the load’s time history across all degrees of freedom of the structure, which can be an extremely labor-intensive task. To address this challenge, this paper proposes a novel method for dimensionality reduction of time-space coupled distributed dynamic loads using Principal Component Analysis (PCA), where the load is represented as the sum of several load principal components. The identification process begins with the application of the Algorithm for Multiple Unknown Signal Extraction (AMUSE) to extract the load distribution matrix. An improved Kalman filter algorithm is then employed for the online identification of the time functions corresponding to the principal components. Sparse regularization is applied to obtain the spatial functions of these components. Finally, the distributed dynamic load is reconstructed by combining the time and spatial functions. In addition, the necessary conditions, computational complexity, and other characteristics of the proposed method are discussed in detail. Numerical results show that the method can accurately identify distributed dynamic load even under noise interference. For complex loading scenarios, the method is still able to produce accurate equivalent loads that replicate the structural response of the actual loads.