多传感器融合的液压系统故障诊断与预测 -机械设计制造及其自动化专业

摘要

液压系统在工业自动化领域中广泛应用，但其复杂性和高精度要求使得故障诊断与预测成为一项极具挑战性的任务。传统的单一传感器诊断方法难以全面捕捉系统的动态特性，导致诊断精度和可靠性不足。本文针对这一问题，提出了一种基于多传感器融合的液压系统故障诊断与预测方法。该方法通过集成压力、流量、温度等多种传感器数据，利用深度学习算法对系统状态进行实时监测和分析。研究首先构建了液压系统的数学模型，并结合实际工况设计了多传感器数据采集方案。随后，采用卷积神经网络和长短期记忆网络相结合的混合模型，对传感器数据进行特征提取和时序分析，从而实现对系统故障的精准识别和趋势预测。实验结果表明，该方法在故障检测准确率上达到了95.8%，较传统方法提升了12.3%，并且在预测精度方面显著优于现有技术。此外，本文提出的融合算法能够有效应对传感器噪声和数据缺失问题，具有较强的鲁棒性。

关键词：液压系统；多传感器融合；故障诊断；深度学习

FAULT DIAGNOSIS AND PREDICTION OF HYDRAULIC SYSTEM BASED ON MULTI-SENSOR FUSION

ABSTRACT

Hydraulic system is widely used in the field of industrial automation, but its complexity and high precision make fault diagnosis and prediction a very challenging task. The traditional single sensor diagnosis method is difficult to capture the dynamic characteristics of the system, resulting in insufficient diagnostic accuracy and reliability. To solve this problem, a fault diagnosis and prediction method of hydraulic system based on multi-sensor fusion is proposed in this paper. The method integrates pressure, flow, temperature and other sensor data, and uses deep learning algorithm to monitor and analyze the system status in real time. Firstly, the mathematical model of the hydraulic system is constructed, and the multi-sensor data acquisition scheme is designed according to the actual working conditions. Then, a hybrid model combining convolutional neural network and long and short term memory network is used to extract feature and time sequence analysis of sensor data, so as to achieve accurate fault identification and trend prediction of the system. The experimental results show that the fault detection accuracy of the proposed method reaches 95.8%, which is 12.3% higher than that of the traditional method, and the prediction accuracy is significantly superior to the prior art. In addition, the fusion algorithm proposed in this paper can effectively deal with sensor noise and data missing problems, and has strong robustness.

KEY WORDS：Hydraulic System; Multi-Sensor Fusion; Fault Diagnosis; Deep Learning

摘要 I

ABSTRACT II

第1章绪论 2

1.1 研究背景及意义 2

1.2 多传感器融合技术在液压系统故障诊断中的应用现状 2

第2章多传感器数据融合技术在液压系统故障诊断中的应用 3

2.1 多传感器数据融合的基本原理与方法 3

2.2 基于多传感器融合的液压系统故障特征提取 3

2.3 多传感器融合在液压系统故障模式识别中的应用 4

第3章液压系统故障预测模型的构建与优化 5

3.1 基于多传感器数据的液压系统故障预测模型设计 5