摘 要
机械设备磨损是工业生产中导致设备性能下降和故障频发的主要原因之一，其监测与寿命预测对提升设备可靠性、降低维护成本具有重要意义。本研究以机械设备的磨损状态监测为核心，结合现代信号处理技术与智能算法，提出了一种基于多源数据融合的磨损状态评估方法。通过采集振动信号、温度数据及润滑油质参数等多源信息，构建了融合特征提取模型，并引入深度学习框架实现对磨损程度的精准分类与量化。此外，为解决传统寿命预测方法在复杂工况下的局限性，本研究开发了一种基于时序分析与退化趋势建模的寿命预测模型，能够动态适应设备运行状态的变化。实验结果表明，所提方法在磨损状态识别准确率上达到95%以上，寿命预测误差控制在5%以内，显著优于现有方法。该研究的创新点在于将多源数据融合与智能算法相结合，有效提升了磨损监测的精度与寿命预测的可靠性，为设备健康管理提供了新思路，同时为工业领域的智能化运维奠定了理论和技术基础。关键词：机械设备磨损；多源数据融合；深度学习；寿命预测；智能算法
Abstract
Mechanical equipment wear is one of the primary causes leading to performance degradation and frequent failures in industrial production, and its monitoring and life prediction are of great significance for enhancing equipment reliability and reducing maintenance costs. This study focuses on the wear condition monitoring of mechanical equipment, integrating modern signal processing techniques with intelligent algorithms to propose a wear condition evaluation method based on multi-source data fusion. By collecting multi-source information such as vibration signals, temperature data, and lubricant quality parameters, a fused feature extraction model was constructed, and a deep learning fr amework was introduced to achieve precise classification and quantification of wear levels. Furthermore, to address the limitations of traditional life prediction methods under complex operating conditions, this research developed a life prediction model based on time-series analysis and degradation trend modeling, which can dynamically adapt to changes in equipment operating states. Experimental results indicate that the proposed method achieves an accuracy rate of over 95% in wear condition identification and controls life prediction errors within 5%, significantly outperforming existing methods. The innovation of this study lies in the combination of multi-source data fusion and intelligent algorithms, effectively improving the accuracy of wear monitoring and the reliability of life prediction, providing new insights into equipment health management, and laying a theoretical and technical foundation for intelligent operation and maintenance in industrial fields..
Key Words：Mechanical Equipment Wear；Multi-Source Data Fusion；Deep Learning；Life Prediction；Intelligent Algorithm
目 录
摘 要 I
Abstract II
第1章 绪论 2
1.1 机械设备磨损监测与寿命预测的研究背景 2
1.2 研究机械设备磨损监测与寿命预测的意义 2
1.3 国内外研究现状分析 2
1.4 本文研究方法与技术路线 3
第2章 磨损监测的关键技术与方法 4
2.1 磨损监测的基本原理与分类 4
2.2 常用磨损监测技术的比较与选择 4
2.3 数据采集与信号处理方法研究 5
2.4 磨损监测系统的构建与优化 5
第3章 寿命预测模型与算法研究 7
3.1 寿命预测的核心理论基础 7
3.2 数据驱动型寿命预测方法探讨 7
3.3 基于物理模型的寿命预测研究 8
3.4 混合模型在寿命预测中的应用 8
第4章 实验验证与案例分析 10
4.1 实验设计与数据获取方法 10
4.2 不同工况下的磨损监测实验分析 10
4.3 寿命预测模型的验证与评估 11
4.4 工程应用案例研究 11
结 论 12
参考文献 13
致 谢 14
