Research on fault diagnosis and intelligent maintenance methods of hydraulic systems in mechanical engineering
机械工程中液压系统故障诊断与智能维护方法研究

摘要

液压系统作为机械工程关键动力装置，其运行稳定性直接关系到设备性能与生产效率。随着系统结构复杂化和使用环境多样化，传统故障诊断方法在精准性与实时性方面面临挑战。融合传感监测技术、信号处理方法与人工智能算法的智能维护手段，已成为提升液压系统运行可靠性的重要方向。围绕液压系统常见故障类型与成因，构建基于多源信息融合的智能诊断模型，并引入预测性维护策略，实现故障的早期预警与动态干预。研究结果表明，智能维护手段可显著提升故障识别准确率与维护响应效率，具有广阔的工程应用前景与推广价值。

Abstract

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.