摘 要
随着工业4.0的推进和智能制造的发展，机械设备的高效运行与可靠性保障成为关键课题，传统故障检测方法因效率低、精度不足等问题已难以满足现代工业需求。为此，本研究旨在构建一种基于人工智能的机械故障检测与预警系统，以提升故障诊断的智能化水平和预测能力。研究采用深度学习算法结合传感器数据采集技术，通过卷积神经网络（CNN）对振动信号进行特征提取，并利用长短时记忆网络（LSTM）实现故障模式的时间序列分析与预测建模。同时，引入迁移学习优化模型训练过程，显著提高了小样本条件下的检测精度。
关键词：机械故障检测 人工智能 深度学习
Abstract
With the promotion of industry 4.0 and the development of intelligent manufacturing, the efficient operation and reliability guarantee of mechanical equipment have become a key topic, and the traditional failure detection methods have been difficult to meet the needs of modern industry due to low efficiency, insufficient accuracy and other problems. To this end, this study aims to construct a mechanical fault detection and early warning system based on artificial intelligence to improve the intelligence level and prediction ability of fault diagnosis. In this study, deep learning algorithm and sensor data acquisition technology are used to extract the features of vibration signals through convolutional neural network (CNN), and realize the time series analysis and prediction modeling of fault mode by using long and short time memory network (LSTM). At the same time, transfer learning was introduced to optimize the model training process, which significantly improves the detection accuracy under small sample conditions.
Keyword:Mechanical Fault Detection Artificial Intelligence Deep Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法与技术路线 1
2人工智能在机械故障检测中的应用基础 2
2.1机械故障检测的基本原理 2
2.2人工智能技术概述及其优势 2
2.3数据采集与预处理的关键技术 3
3基于人工智能的故障检测算法设计 4
3.1故障特征提取方法研究 4
3.2深度学习模型在故障检测中的应用 4
3.3算法优化与性能评估 5
4预警系统的设计与实现 5
4.1预警系统的架构设计 5
4.2实时数据监控与异常识别 6
4.3预警策略与决策支持机制 6
结论 7
参考文献 8
致谢 9
随着工业4.0的推进和智能制造的发展，机械设备的高效运行与可靠性保障成为关键课题，传统故障检测方法因效率低、精度不足等问题已难以满足现代工业需求。为此，本研究旨在构建一种基于人工智能的机械故障检测与预警系统，以提升故障诊断的智能化水平和预测能力。研究采用深度学习算法结合传感器数据采集技术，通过卷积神经网络（CNN）对振动信号进行特征提取，并利用长短时记忆网络（LSTM）实现故障模式的时间序列分析与预测建模。同时，引入迁移学习优化模型训练过程，显著提高了小样本条件下的检测精度。
关键词：机械故障检测 人工智能 深度学习
Abstract
With the promotion of industry 4.0 and the development of intelligent manufacturing, the efficient operation and reliability guarantee of mechanical equipment have become a key topic, and the traditional failure detection methods have been difficult to meet the needs of modern industry due to low efficiency, insufficient accuracy and other problems. To this end, this study aims to construct a mechanical fault detection and early warning system based on artificial intelligence to improve the intelligence level and prediction ability of fault diagnosis. In this study, deep learning algorithm and sensor data acquisition technology are used to extract the features of vibration signals through convolutional neural network (CNN), and realize the time series analysis and prediction modeling of fault mode by using long and short time memory network (LSTM). At the same time, transfer learning was introduced to optimize the model training process, which significantly improves the detection accuracy under small sample conditions.
Keyword:Mechanical Fault Detection Artificial Intelligence Deep Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法与技术路线 1
2人工智能在机械故障检测中的应用基础 2
2.1机械故障检测的基本原理 2
2.2人工智能技术概述及其优势 2
2.3数据采集与预处理的关键技术 3
3基于人工智能的故障检测算法设计 4
3.1故障特征提取方法研究 4
3.2深度学习模型在故障检测中的应用 4
3.3算法优化与性能评估 5
4预警系统的设计与实现 5
4.1预警系统的架构设计 5
4.2实时数据监控与异常识别 6
4.3预警策略与决策支持机制 6
结论 7
参考文献 8
致谢 9
