Intelligent Fault Diagnosis of Hydraulic Systems based on Multi-Sensor Fusion and Deep Learning

Hydraulic systems play a central role in the transmission and control of various industrial equipment, and the consequences of failures can be severe. The high pressure and closed nature of hydraulic systems, coupled with poor measurability of parameters, limit the applicability of many fault diagnosis methods. In order to achieve automatic fusion of multi-sensor data and accurate fault diagnosis, this paper proposes a novel intelligent fault diagnosis model (IFDM) that combines Variational Mode Decomposition (VMD) with residual networks incorporating attention mechanisms. Utilizing adaptive VMD along with multiscale dispersion entropy allows for the automatic extraction of features from multi-sensor data without the need for specialized knowledge, making it more suitable for industrial applications. The residual networks with attention mechanisms allocate weights to each channel, enabling the model to better learn the relationships between channels and enhance its focus on different fault features. Experimental results demonstrate that the proposed method can accurately diagnose various levels of faults in multiple components of hydraulic systems, achieving a classification accuracy exceeding 99% with superior generalization capabilities.