摘 要
随着工业4.0的推进和智能制造技术的发展，机械故障预测已成为提升设备可靠性和降低维护成本的关键环节。本研究旨在探索基于机器学习的机械故障预测技术，以实现对复杂机械设备运行状态的精准评估与早期预警。通过整合多种传感器数据，采用深度学习模型与传统机器学习算法相结合的方法，构建了多维度特征提取与故障预测框架。研究中引入了时间序列分析和异常检测技术，优化了数据预处理流程，并设计了一种融合注意力机制的神经网络模型以提高预测精度。实验结果表明，该方法在多种实际工况下表现出优异的性能，相较于传统方法，其预测准确率提升了约15%，同时显著缩短了故障响应时间。此外，本研究提出的数据增强策略有效缓解了样本不平衡问题，为小样本场景下的故障预测提供了新思路。
关键词：机械故障预测 机器学习 深度学习
Abstract
With the advancement of industry 4.0 and the development of intelligent manufacturing technology, mechanical failure prediction has become a key link to improve the reliability of equipment and reduce maintenance costs. The present study aims to explore machine learning-based mechanical fault prediction techniques to achieve accurate assessment and early warning of the operating status of complex mechanical devices. By integrating multiple sensor data and combining deep learning model with traditional machine learning algorithm, a multi-dimensional feature extraction and fault prediction fr amework is constructed. Time series analysis and anomaly detection techniques were introduced to optimize the data preprocessing process, and a neural network model integrating the attention mechanism was designed to improve the prediction accuracy. The experimental results show that the proposed method shows excellent performance in many practical conditions, compared with the traditional method, the prediction accuracy improves by about 15%, and the fault response time is significantly shortened. Moreover, the data enhancement strategy proposed in this study effectively alleviates the sample imbalance problem and provides a new idea for failure prediction in small sample scenarios.
Keyword:Mechanical Fault Prediction Machine Learning 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本文研究方法概述 2
2机械故障预测的数据处理技术 3
2.1数据采集与预处理方法 3
2.2特征提取与选择策略 4
2.3数据质量评估与优化 4
3机器学习算法在故障预测中的应用 5
3.1常见机器学习算法介绍 5
3.2算法选择与模型构建 5
3.3模型训练与性能评估 6
4故障预测系统的实现与验证 6
4.1预测系统架构设计 6
4.2实验案例分析与结果讨论 7
4.3系统性能优化与改进方向 7
结论 8
参考文献 9
致谢 10
随着工业4.0的推进和智能制造技术的发展，机械故障预测已成为提升设备可靠性和降低维护成本的关键环节。本研究旨在探索基于机器学习的机械故障预测技术，以实现对复杂机械设备运行状态的精准评估与早期预警。通过整合多种传感器数据，采用深度学习模型与传统机器学习算法相结合的方法，构建了多维度特征提取与故障预测框架。研究中引入了时间序列分析和异常检测技术，优化了数据预处理流程，并设计了一种融合注意力机制的神经网络模型以提高预测精度。实验结果表明，该方法在多种实际工况下表现出优异的性能，相较于传统方法，其预测准确率提升了约15%，同时显著缩短了故障响应时间。此外，本研究提出的数据增强策略有效缓解了样本不平衡问题，为小样本场景下的故障预测提供了新思路。
关键词：机械故障预测 机器学习 深度学习
Abstract
With the advancement of industry 4.0 and the development of intelligent manufacturing technology, mechanical failure prediction has become a key link to improve the reliability of equipment and reduce maintenance costs. The present study aims to explore machine learning-based mechanical fault prediction techniques to achieve accurate assessment and early warning of the operating status of complex mechanical devices. By integrating multiple sensor data and combining deep learning model with traditional machine learning algorithm, a multi-dimensional feature extraction and fault prediction fr amework is constructed. Time series analysis and anomaly detection techniques were introduced to optimize the data preprocessing process, and a neural network model integrating the attention mechanism was designed to improve the prediction accuracy. The experimental results show that the proposed method shows excellent performance in many practical conditions, compared with the traditional method, the prediction accuracy improves by about 15%, and the fault response time is significantly shortened. Moreover, the data enhancement strategy proposed in this study effectively alleviates the sample imbalance problem and provides a new idea for failure prediction in small sample scenarios.
Keyword:Mechanical Fault Prediction Machine Learning 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本文研究方法概述 2
2机械故障预测的数据处理技术 3
2.1数据采集与预处理方法 3
2.2特征提取与选择策略 4
2.3数据质量评估与优化 4
3机器学习算法在故障预测中的应用 5
3.1常见机器学习算法介绍 5
3.2算法选择与模型构建 5
3.3模型训练与性能评估 6
4故障预测系统的实现与验证 6
4.1预测系统架构设计 6
4.2实验案例分析与结果讨论 7
4.3系统性能优化与改进方向 7
结论 8
参考文献 9
致谢 10
