基于机器学习的液压系统故障模式识别研究 -机械设计制造及其自动化专业

摘要

液压系统作为现代工业设备的核心组成部分，其故障模式的准确识别对于保障设备运行稳定性和安全性具有重要意义。然而，传统的故障诊断方法依赖于专家经验和简单的统计分析，难以应对复杂多变的故障模式。随着机器学习技术的快速发展，基于数据驱动的故障模式识别方法逐渐成为研究热点。本研究旨在通过机器学习算法对液压系统的故障模式进行自动识别和分类，以提高诊断的准确性和效率。研究首先收集了大量液压系统的运行数据，并对其进行预处理和特征提取。随后，采用支持向量机、随机森林和深度神经网络等多种机器学习模型对数据进行训练和测试。实验结果表明，深度神经网络在故障模式识别任务中表现最为优异，准确率达到95.2%，显著高于传统方法。

关键词：液压系统；故障模式识别；机器学习；深度神经网络

RESEARCH ON FAULT PATTERN RECOGNITION OF HYDRAULIC SYSTEM BASED ON MACHINE LEARNING

ABSTRACT

Hydraulic system is the core component of modern industrial equipment, the accurate identification of its fault mode is of great significance to ensure the stability and safety of equipment operation. However, the traditional fault diagnosis methods rely on expert experience and simple statistical analysis, and it is difficult to deal with complex and changeable fault modes. With the rapid development of machine learning technology, data-driven fault pattern recognition method has gradually become a research hotspot. The purpose of this research is to automatically identify and classify the fault mode of hydraulic system through machine learning algorithm, so as to improve the accuracy and efficiency of diagnosis. Firstly, we collect a large number of operating data of hydraulic system, and carry out pretreatment and feature extraction. The data is then trained and tested using a variety of machine learning models such as support vector machines, random forests, and deep neural networks. The experimental results show that the deep neural network performs best in the fault pattern recognition task with an accuracy of 95.2%, which is significantly higher than the traditional method.

KEY WORDS：Hydraulic System; Fault Pattern Recognition; Machine Learning; Deep Neural Network

摘要 I

ABSTRACT II

第1章引言 2

第2章基于机器学习的液压系统故障特征提取与预处理 3

2.1 液压系统故障特征的分类与选择 3

2.2 数据预处理方法及其在故障模式识别中的应用 3

2.3 特征降维技术在液压系统故障识别中的优化策略 4

第3章基于机器学习的液压系统故障模式分类模型构建 5

3.1 常用机器学习算法在液压系统故障分类中的适用性分析 5

3.2 模型参数优化与性能评估方法 5

3.3 多分类器集成技术在液压系统故障识别中的应用研究 6