Output-only modal analysis for non-synchronous data using stochastic sub-space identification. (1st March 2021)

• Record Type: Journal Article
• Authors: Lu, Lin-Jun; Zhou, Hua-Fei; Ni, Yi-Qing; Dai, Fei
• Is Part Of: Engineering structures. Volume 230(2021)
• Publication Date: 2021-03-01
• Subjects: Modal identification -- Stochastic subspace identification -- Non-synchronous -- Mean phase deviation -- Structural health monitoring; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2020.111702
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Integrated time lag estimation and modal extraction for asynchronous measurements. Mean phase deviation (MPD) for determining actual mode shape components. Accelerate computation by making use of periodicity of MPD. Validated by asynchronous accelerations captured during a ship-bridge collision. Abstract: The stochastic subspace identification (SSI) method has been recognized as the most dominant and popular system identification technique in the time domain. Nevertheless, it cannot cope with the non-synchronicity of dynamic response measurements that happens sometimes in structural health monitoring practice. To overcome this, this study proposes a strategy for the SSI algorithm to realize system identification from non-synchronous dynamic response measurements. The modal identification is carried out in a pairwise manner by pairing a time-shifted signal with a common reference signal. The state space model is employed to fit the pair of reference and time-shifted signals and the SSI algorithm is exploited to extract the modal parameters. The core of the strategy is the tactical usage of the mean phase deviation (MPD) of the mode for seeking out the actual time lag as well as the actual mode shape components simultaneously. By so doing, the strategy fulfills integrated time lag estimation and modal extraction for non-synchronous dynamic response measurements. Furthermore, the strategy also overcomes the difficulty to determine the model order with the use of the stabilization diagram and reduces the computational cost substantially with the help of the periodicity of the MPD. To examine the performance of the strategy, intensive validations are conducted by making use of the non-synchronous acceleration measurements of the Jiangyin Bridge subjected to a ship collision and the artificially misaligned acceleration measurements of the Canton Tower struck by an earthquake. Both the time lags and the mode shapes identified by the strategy can be well validated, indicating that the proposed strategy is competent for modal identification of non-synchronous dynamic response measurements.