摘 要
随着现代农业的发展，农业机械的高效运行对农业生产至关重要，而传统故障诊断方法存在效率低、精度差等问题。为此，本研究旨在开发一种基于智能算法的农业机械故障诊断系统，以提高故障诊断的准确性和时效性。该系统融合了物联网技术、大数据分析和机器学习算法，通过在农机关键部位安装传感器采集运行数据，利用神经网络模型对数据进行深度学习训练，实现故障模式识别与预警。经过实地测试验证，该系统能够实时监测农机工作状态，故障诊断准确率达到92%以上，平均诊断时间缩短至3分钟以内。相比传统方法，本系统不仅提高了诊断效率和准确性，还实现了远程监控与维护功能，为农机智能化管理提供了有效解决方案，具有重要的应用价值和推广前景。
关键词：农业机械故障诊断；智能算法；物联网技术
ABSTRACT
With the development of modern agriculture, the efficient operation of agricultural machinery has become crucial for agricultural production, while traditional fault diagnosis methods suffer from low efficiency and poor accuracy. To address these issues, this study aims to develop an intelligent algorithm-based fault diagnosis system for agricultural machinery to enhance the accuracy and timeliness of fault diagnosis. The system integrates Internet of Things (IoT) technology, big data analytics, and machine learning algorithms, employing sensors installed on critical components of agricultural machinery to collect operational data. Neural network models are utilized for deep learning training of the data to achieve fault pattern recognition and early warning. Verified through field tests, the system can monitor the working status of agricultural machinery in real-time, achieving a fault diagnosis accuracy rate of over 92%, with the average diagnosis time reduced to within 3 minutes. Compared to traditional methods, this system not only improves diagnostic efficiency and accuracy but also realizes remote monitoring and maintenance functions, providing an effective solution for intelligent management of agricultural machinery, which holds significant application value and prospects for promotion.
KEY WORDS：Agricultural Machinery Fault Diagnosis；Intelligent Algorithm；Internet Of Things Technology
目 录
摘 要 I
ABSTRACT II
第一章 绪论 1
1.1 农业机械故障诊断背景与意义 1
1.2 国内外研究现状综述 1
第二章 农业机械故障特征分析 3
2.1 故障类型与成因剖析 3
2.2 关键部件故障模式 3
2.3 故障数据采集技术 4
第三章 智能诊断系统设计与实现 5
3.1 系统架构设计原则 5
3.2 核心算法选型与优化 5
3.3 硬件平台搭建方案 6
第四章 系统测试与性能评估 7
4.1 测试环境构建方法 7
4.2 诊断准确率分析 7
4.3 实际应用效果评价 8
结 论 9
参考文献 10
致 谢 11
随着现代农业的发展，农业机械的高效运行对农业生产至关重要，而传统故障诊断方法存在效率低、精度差等问题。为此，本研究旨在开发一种基于智能算法的农业机械故障诊断系统，以提高故障诊断的准确性和时效性。该系统融合了物联网技术、大数据分析和机器学习算法，通过在农机关键部位安装传感器采集运行数据，利用神经网络模型对数据进行深度学习训练，实现故障模式识别与预警。经过实地测试验证，该系统能够实时监测农机工作状态，故障诊断准确率达到92%以上，平均诊断时间缩短至3分钟以内。相比传统方法，本系统不仅提高了诊断效率和准确性，还实现了远程监控与维护功能，为农机智能化管理提供了有效解决方案，具有重要的应用价值和推广前景。
关键词：农业机械故障诊断；智能算法；物联网技术
ABSTRACT
With the development of modern agriculture, the efficient operation of agricultural machinery has become crucial for agricultural production, while traditional fault diagnosis methods suffer from low efficiency and poor accuracy. To address these issues, this study aims to develop an intelligent algorithm-based fault diagnosis system for agricultural machinery to enhance the accuracy and timeliness of fault diagnosis. The system integrates Internet of Things (IoT) technology, big data analytics, and machine learning algorithms, employing sensors installed on critical components of agricultural machinery to collect operational data. Neural network models are utilized for deep learning training of the data to achieve fault pattern recognition and early warning. Verified through field tests, the system can monitor the working status of agricultural machinery in real-time, achieving a fault diagnosis accuracy rate of over 92%, with the average diagnosis time reduced to within 3 minutes. Compared to traditional methods, this system not only improves diagnostic efficiency and accuracy but also realizes remote monitoring and maintenance functions, providing an effective solution for intelligent management of agricultural machinery, which holds significant application value and prospects for promotion.
KEY WORDS：Agricultural Machinery Fault Diagnosis；Intelligent Algorithm；Internet Of Things Technology
目 录
摘 要 I
ABSTRACT II
第一章 绪论 1
1.1 农业机械故障诊断背景与意义 1
1.2 国内外研究现状综述 1
第二章 农业机械故障特征分析 3
2.1 故障类型与成因剖析 3
2.2 关键部件故障模式 3
2.3 故障数据采集技术 4
第三章 智能诊断系统设计与实现 5
3.1 系统架构设计原则 5
3.2 核心算法选型与优化 5
3.3 硬件平台搭建方案 6
第四章 系统测试与性能评估 7
4.1 测试环境构建方法 7
4.2 诊断准确率分析 7
4.3 实际应用效果评价 8
结 论 9
参考文献 10
致 谢 11
