Application of adaptable functional series vector time-dependent autoregressive model for extraction of real modal parameters for identification of time-varying systems. (June 2017)

• Record Type: Journal Article
• Authors: Li, Wenchao; Vu, Viet-Hung; Liu, Zhaoheng; Thomas, Marc; Hazel, Bruce
• Is Part Of: Measurement. Volume 103(2017)
• Page Start: 143
• Page End: 156
• Publication Date: 2017-06
• Subjects: Time-varying systems -- Operational modal analysis -- Adaptable functional series vector time-dependent autoregressive model -- Extraction of real modal parameters -- Eigen-decomposition of state matrix -- Modal signal to noise; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2017.02.027
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: This paper presents a method for extraction of real modal parameters for identification of time-varying systems using adaptable functional series vector time-dependent autoregressive (AFS-VTAR) model. In the practical application of AFS-VTAR model, both real modal parameters and computational ones may mix together in the identified results. In order to extract the real ones from computational ones, a new method based on the eigen-decomposition of the state matrix constructed in AFS-VTAR is proposed. The efficiency of the proposed method is successfully demonstrated first through numerical simulations applied to a time-varying 3-degree-of-freedom system and then experimentally by applying it to a flexible robot in motion.