Noninvasive pressure monitoring using acoustic resonance spectroscopy and machine learning

Monitoring pressure inside hermetically sealed vessels typically relies on devices that have direct contact with the fluid inside. Gaining this access requires a hole through the wall of the vessel, which creates potential for leaks, ruptures, and complete failures. To solve this, noninvasive solutions utilize external sensors that relate vessel-wall behavior to internal pressure. However, existing noninvasive techniques require permanently attaching sensors to a unique vessel and then monitoring for changes in the vessel. We present a noninvasive pressure monitoring technique based on acoustic resonance spectroscopy (ARS) and machine learning (ML) that enables estimating pressure in a vessel similar to those it was trained on and does not require sensors to be permanently attached. We train k-nearest neighbor (KNN) regressor models using experimentally gathered acoustic resonance spectra to estimate the pressure in six stainless-steel vessels. We demonstrate accurate estimation of the pressure inside the vessels when training and testing using spectra taken exclusively from an individual vessel, and when performing cross-validation between vessels. The acoustic technique presented in this paper finds broad applications across industry to monitor pressure in systems where having permanent sensors is undesirable, such as complicated pneumatic systems, vacuum sealed foods, and more.