摘 要
随着制造业的快速发展，数控机床作为现代工业生产的核心设备，其故障诊断与维修技术成为保障生产效率和产品质量的关键。本文针对数控机床故障诊断与维修中的难点问题展开研究，旨在提高故障诊断的准确性和维修的有效性。通过对国内外相关研究现状的分析，发现现有方法在复杂工况下的适应性不足、诊断精度不高且缺乏对多源信息融合的有效利用。为此，提出基于多源信息融合的故障诊断模型，该模型综合了振动信号、温度信号以及电流信号等多源信息，并引入深度学习算法进行特征提取与模式识别，实现了对数控机床典型故障的精准定位。同时，构建了以故障树分析为基础的维修决策支持系统，根据故障诊断结果提供最优维修方案。实验结果表明，所提出的故障诊断模型能够有效提高故障识别率，平均准确率达到92%以上；维修决策支持系统可显著缩短维修时间，降低维修成本约30%。
关键词：数控机床故障诊断 多源信息融合 深度学习
Abstract
With the rapid development of manufacturing industry, CNC machine tool as the core equipment of modern industrial production, its fault diagnosis and maintenance technology has become the key to ensure the production efficiency and product quality. This paper studies the difficult problems in the fault diagnosis and maintenance of CNC machine tools, aiming to improve the accuracy of fault diagnosis and the effectiveness of maintenance. Through the analysis of the relevant research status at home and abroad, it is found that the existing methods have insufficient adaptability, low diagnostic accuracy and lack of effective utilization of multi-source information fusion under complex working conditions. Therefore, a fault diagnosis model based on multi-source information fusion is proposed, which integrates multi-source information such as vibration signal, temperature signal and current signal, and introduces deep learning algorithm for feature extraction and pattern recognition, so as to realize the accurate positioning of typical faults of CNC machine tools. At the same time, a maintenance decision support system based on fault tree analysis is constructed to provide the optimal maintenance scheme according to the fault diagnosis results. The experimental results show that the proposed fault diagnosis model can effectively improve the fault identification rate with the average accuracy of over 92%; the maintenance decision support system can significantly shorten the maintenance time and reduce the maintenance cost by about 30%.
Keyword:Cnc Machine Tool Fault Diagnosis Multi-source Information Fusion Deep Learning
目 录
1绪论 1
1.1研究背景 1
1.2研究的意义与价值 1
1.3国内外研究现状综述 1
1.4本文的研究方法概述 2
2数控机床故障类型分析 2
2.1常见机械故障剖析 2
2.2电气系统故障探究 3
2.3控制系统故障解析 3
2.4故障类型的关联性 4
3故障诊断技术研究 4
3.1诊断技术原理阐述 4
3.2在线监测与预警机制 5
3.3数据采集与信号处理 5
3.4智能诊断算法应用 6
4维修技术及策略探讨 6
4.1预防性维修措施 6
4.2故障修复技术手段 7
4.3维修后的性能评估 8
4.4维修资源优化配置 8
结论 9
参考文献 10
致谢 11
随着制造业的快速发展，数控机床作为现代工业生产的核心设备，其故障诊断与维修技术成为保障生产效率和产品质量的关键。本文针对数控机床故障诊断与维修中的难点问题展开研究，旨在提高故障诊断的准确性和维修的有效性。通过对国内外相关研究现状的分析，发现现有方法在复杂工况下的适应性不足、诊断精度不高且缺乏对多源信息融合的有效利用。为此，提出基于多源信息融合的故障诊断模型，该模型综合了振动信号、温度信号以及电流信号等多源信息，并引入深度学习算法进行特征提取与模式识别，实现了对数控机床典型故障的精准定位。同时，构建了以故障树分析为基础的维修决策支持系统，根据故障诊断结果提供最优维修方案。实验结果表明，所提出的故障诊断模型能够有效提高故障识别率，平均准确率达到92%以上；维修决策支持系统可显著缩短维修时间，降低维修成本约30%。
关键词：数控机床故障诊断 多源信息融合 深度学习
Abstract
With the rapid development of manufacturing industry, CNC machine tool as the core equipment of modern industrial production, its fault diagnosis and maintenance technology has become the key to ensure the production efficiency and product quality. This paper studies the difficult problems in the fault diagnosis and maintenance of CNC machine tools, aiming to improve the accuracy of fault diagnosis and the effectiveness of maintenance. Through the analysis of the relevant research status at home and abroad, it is found that the existing methods have insufficient adaptability, low diagnostic accuracy and lack of effective utilization of multi-source information fusion under complex working conditions. Therefore, a fault diagnosis model based on multi-source information fusion is proposed, which integrates multi-source information such as vibration signal, temperature signal and current signal, and introduces deep learning algorithm for feature extraction and pattern recognition, so as to realize the accurate positioning of typical faults of CNC machine tools. At the same time, a maintenance decision support system based on fault tree analysis is constructed to provide the optimal maintenance scheme according to the fault diagnosis results. The experimental results show that the proposed fault diagnosis model can effectively improve the fault identification rate with the average accuracy of over 92%; the maintenance decision support system can significantly shorten the maintenance time and reduce the maintenance cost by about 30%.
Keyword:Cnc Machine Tool Fault Diagnosis Multi-source Information Fusion Deep Learning
目 录
1绪论 1
1.1研究背景 1
1.2研究的意义与价值 1
1.3国内外研究现状综述 1
1.4本文的研究方法概述 2
2数控机床故障类型分析 2
2.1常见机械故障剖析 2
2.2电气系统故障探究 3
2.3控制系统故障解析 3
2.4故障类型的关联性 4
3故障诊断技术研究 4
3.1诊断技术原理阐述 4
3.2在线监测与预警机制 5
3.3数据采集与信号处理 5
3.4智能诊断算法应用 6
4维修技术及策略探讨 6
4.1预防性维修措施 6
4.2故障修复技术手段 7
4.3维修后的性能评估 8
4.4维修资源优化配置 8
结论 9
参考文献 10
致谢 11
