On uncertainty propagation in mass, damping and stiffness matrices identification of mechanical systems

The accuracy in estimating the mass, damping and stiffness matrices for mechanical systems depends on the error propagation through the stages involved in the parameter identification, i.e. excitation and response measurements, signal processing and modeling stages. Robust algorithms are available to estimate the systeḿs parameters in the presence of "noisy" measurements. However, uncertainties in the identified parameters of mechanical systems have not been usually reported or have simply been overlooked in the identification strategy. An overall uncertainty occurs for each identified parameter, and it may be defined in terms of error propagation. The recognition of relevant error contributions is the key to accomplishing parameter error estimation in the identification process, a task that may imply subtle aspects. An approach is proposed for uncertainty estimation in mass, stiffness and damping matrices for linearized mechanical systems. This approach is formulated as an extension of the accepted practice for evaluating experimental uncertainty for a scalar measurand. Typical error sources throughout the identification stages are also discussed. The suggested approach may be applied to identify mechanical systems in the frequency domain, and is independent of the algorithm used to estimate the system parameters. Practical limitations of the suggested approach are also discussed.