Fault Prediction in High-Efficiency Petroleum Machinery Production

This paper introduces a fault diagnosis and prediction framework for petroleum machinery production systems, addressing the need for more efficient and reliable fault handling in the face of complex signals.Utilizing the Complete Ensemble Empirical Mode Decomposition (CEEMD) and permutation entropy, it extracts signal features to analyse system dynamics.An Adaptive Variational Mode Decomposition (VMD) and optimized Extreme Learning Machine (ELM) simulation model enhances diagnostic accuracy through signal processing and fast learning capabilities.This approach not only elevates fault diagnosis precision but also supports the maintenance and health management of petroleum machinery systems, offering significant theoretical and practical benefits.