Abstract Summary
System identification on operational structures is made difficult by the lack of information regarding the structure and the input force. Output-only system identification methods exist as a way of inferring the system and input force, given the output of the system which can be readily measured. Within the context of identifying structural systems, the process of determining the properties of a system from output-only data falls under the banner of Operational Modal Analysis (OMA). This paper investigates the use of the Gaussian Process Convolution Model (GPCM) as an output only system identification tool for structural systems. The form of the model assumes a priori that the observed data arise as the result of a convolution between an unknown linear filter and an unobserved white noise process, where each of these are modelled as a GP. The GPCM infers both the linear time filter (which is the impulse response function, i.e. Green’s function, of the system) and driving white noise process in a Bayesian probabilistic fashion with an approximate posterior over both signals. The GPCM is demonstrated on a synthetic case study, and benchmarked against other available OMA techniques and against a non-dynamic GP model. It will additionally be shown how this uncertainty can be propagated onto the modal properties of the system.
