Adaptive Prediction of Compressor Cylinder Pressure Dynamics Using a Physics-Guided VAE-CNN State Space Model

Abstract

Air compressor valves are prone to mechanical wear and elastic fatigue during long-term operation, often leading to poor sealing or leakage. Such leakage is a typical failure mode of reciprocating compressors, introducing strong nonlinearities into cylinder pressure dynamics and significantly increasing the difficulty of state monitoring. To address this issue, this paper presents an adaptive prediction method for compressor cylinder pressure dynamics under valve leakage failure, based on a physics-guided Variational Autoencoder Convolutional Neural Network State Space Model (VAE-CNN-SSM). In this framework, a VAE with embedded physical information is employed to construct the state equation and generate latent variables reflecting valve motion degradation, while a CNN-based observation equation is established to map latent states to cylinder pressure. This hybrid modeling strategy enables accurate prediction of cylinder pressure dynamics and effective characterization of valve degradation behaviors under valve leakage failure conditions. Comparative experiments against conventional models demonstrate that the proposed method achieves superior predictive accuracy, robustness, and generalization. These findings provide a new approach for analyzing valve leakage failures and offer technical support for condition monitoring, health management, and predictive maintenance of reciprocating compressors.