Application of virtual sensing to an outdoor air-conditioning compressor unit

This study demonstrates an acceleration-based virtual sensing (VS) approach for estimating low-frequency vibrations in a commercial air-conditioning compressor unit. Frequencies corresponding to the fundamental rotational frequency (1f) of the compressor and its higher harmonics up to the sixth harmonic (6f) dominate the spectrum and are tied to reliability issues in piping, yet are difficult to monitor due to the system’s geometric complexity. A linear Kalman filter (KF) was implemented under a zero-input assumption (u = 0) during quasi-steady intervals, explicitly avoiding reliance on high-fidelity finite-element (FE) models or known excitations. A speed-sweep test on a full-scale outdoor unit was conducted; estimated accelerations were benchmarked against independent measurements using a cross signature scale factor (CSF). With the observation noise parameter α fixed at 100 and the process noise parameter β tuned, the method achieved an average CSF of 0.79 (0–200 Hz), while capturing the dominant resonance near 122 Hz and overall trends despite model discrepancies. These results indicate that accurate low-frequency vibration estimation is feasible with limited sensors and imperfect FE models, supporting scalable diagnostics and predictive maintenance in HVAC equipment and other vibration-sensitive products.