Structural dynamic response reconstruction with multi-type sensors, unknown input, and rank deficient feedthrough matrix. (15th March 2023)

• Record Type: Journal Article
• Authors: Zhu, Zimo; Zhu, Songye; Wang, You-Wu; Ni, Yi-Qing
• Is Part Of: Mechanical systems and signal processing. Volume 187(2023)
• Publication Date: 2023-03-15
• Subjects: Optimal filtering -- Response reconstruction -- Input estimation -- Unknown input -- Sensor data fusion; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109935
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Reconstruct structural dynamic response with unknown input and limited accelerometers. Release the requirement for a full-rank direct feedthough matrix in response reconstruction. Introduce unified linear input and state estimator (ULISE) into dynamic response reconstruction. ULISE is a generalization and improvement of existing response reconstruction filters. Feasibility of ULISE is validated analytically, numerically, and experimentally. Abstract: This paper presents a novel algorithm that reconstructs structural responses under unknown inputs and rank-deficient feedthrough matrix conditions. The algorithm eliminates one of the major constraints of existing filters (i.e., the requirement of a full-rank matrix), allowing the number of accelerometers required in real applications to be reduced. A unified linear input and state estimator (ULISE) is introduced into structural response reconstruction for the first time. The ULISE requires no prior assumptions on the time histories of unknown inputs. The direct feedthrough matrix can either be rank-deficient or full-column-rank. Moreover, the ULISE eliminates the time delay problem in the input reconstruction based on displacement measurement. The effectiveness of the proposed ULISE-based structural response reconstruction algorithm is evaluated and validated through numerical simulations and laboratory tests. The proposed algorithm achieves reasonable joint input–state estimation even under rank-deficient feedthrough matrix conditions and can be regarded as a generalized and improved version of the existing response reconstruction filters.