农机设备故障预测与健康管理方法 -农业机械化及其自动化专业

摘要

随着农业机械化水平的不断提升，农机设备在现代农业生产中扮演着愈发重要的角色。然而，由于长期高强度作业和复杂环境的影响，农机设备故障频发，严重影响了农业生产的效率和安全性。因此，如何有效预测农机设备的故障并进行健康管理成为当前研究的热点问题。本研究旨在通过融合多源数据和先进的人工智能技术，提出一种基于机器学习的农机设备故障预测与健康管理方法。首先，通过对农机设备运行状态数据的采集与分析，构建了多维度的特征集；其次，采用深度学习算法对设备的历史故障数据进行建模，实现了对潜在故障的早期预警；最后，结合健康管理理论，设计了一套动态维护策略，以优化设备的维护周期和资源配置。研究结果表明，该方法能够显著提高农机设备的故障预测准确率，并在实际应用中有效降低了维护成本和停机时间。

关键词：农机设备故障预测；健康管理；深度学习；多源数据融合

FAILURE PREDICTION AND HEALTH MANAGEMENT OF AGRICULTURAL MACHINERY AND EQUIPMENT

ABSTRACT

With the continuous improvement of the level of agricultural mechanization, agricultural machinery and equipment play an increasingly important role in modern agricultural production. However, due to the long-term high-intensity operation and complex environment, agricultural machinery and equipment failure frequently, seriously affecting the efficiency and safety of agricultural production. Therefore, how to effectively predict the failure of agricultural machinery and equipment and carry out health management has become a hot issue in current research. This study aims to propose a machine learning-based failure prediction and health management method for agricultural machinery by integrating multi-source data and advanced artificial intelligence technology. Firstly, a multi-dimensional feature set is constructed by collecting and analyzing the running state data of agricultural machinery and equipment. Secondly, the deep learning algorithm is used to model the historical fault data of the equipment to realize the early warning of potential faults. Finally, based on the theory of health management, a set of dynamic maintenance strategy is designed to optimize the maintenance cycle and resource allocation of equipment. The results show that this method can significantly improve the accuracy of fault prediction of agricultural machinery equipment, and effectively reduce the maintenance cost and downtime in practical application.

KEY WORDS：Agricultural Machinery And Equipment Failure Prediction; Health Management; Deep Learning; Multi-Source Data Fusion

摘要 I

ABSTRACT II

第1章引言 2

第2章农机设备故障预测的理论基础 3

2.1 故障预测的基本概念与分类 3

2.2 农机设备故障的主要类型及特征 3

第3章农机设备健康管理的技术框架 5

3.1 健康管理系统的组成与功能 5

3.2 传感器技术在农机设备中的应用 5

3.3 数据采集与预处理方法研究 5

第4章农机设备故障预测与健康管理的实践应用 7