Exploring the application of a Bayesian framework in transfer path analysis

Transfer path analysis (TPA) is a methodology widely used in transmission identification of vibration and noise. Among the TPA families, the two prevailing approaches are classical TPA with the matrix inversion method and operational transfer path analysis (OTPA). Both methods rely on measurements within costly and complicated overdetermined systems. These experiment-based methods are also limited by sufficient space for the sensor mounting, which can result in difficulty when analyzing the transfer path in small objects. In this paper, we use an experimental setup that mimics an engine mounted on a suspension with resilient connections. The severity of errors stemming from the application of underdetermined measurement systems is assessed, alongside an exploration of how the Bayesian framework can be integrated into the analysis to mitigate errors and enhance the performance of TPA.