Journal of Engineering Research

As a key power unit in mechanical engineering, the operational stability of hydraulic systems is directly related to equipment performance and production efficiency. With the increasing complexity of system structures and diversification of usage environments, traditional fault diagnosis methods face challenges in terms of accuracy and real-time performance. Intelligent maintenance methods that integrate sensor monitoring technology, signal processing techniques, and artificial intelligence algorithms have become an important direction for improving the operational reliability of hydraulic systems. This study focuses on the common types of faults and their causes in hydraulic systems, constructs an intelligent diagnostic model based on multi-source information fusion, and introduces predictive maintenance strategies to achieve early warning and dynamic intervention for faults. The research results indicate that intelligent maintenance methods can significantly improve fault identification accuracy and maintenance response efficiency, offering broad prospects for engineering application and promotion value.

Hydraulic system; Fault diagnosis; Intelligent maintenance; Information fusion; Predictive maintenance