Vibration fault diagnosis analysis of high-pressure common rail diesel engines based on empirical mode decomposition and autocorrelation algorithms

Abstract

As the degree of power enhancement in internal combustion engines increases, the engines face significantly greater mechanical and thermal loads, presenting more challenges to the noise, vibration, harshness (NVH) performance and reliability of the entire machine. Effective NVH control requires accurate identification and analysis of vibration and noise sources, and a prerequisite for accurately identifying these sources is conducting thorough fault detection of related components. With the help of LabVIEW software, a system for empirical mode decomposition (EMD) and autocorrelation analysis was developed. Test results show that the autocorrelation signal analysis of the oil pan, generator, and cylinder head cover at 800 r/min, compared with the EMD component signals, resulted in no clutter signals, confirming the absence of fault waves in the components.