工业物联网环境下设备状态监测系统 -电气工程及其自动化专业

摘要

随着工业物联网技术的快速发展，设备状态监测系统在工业生产中的重要性日益凸显。本研究旨在构建一个高效、可靠的工业物联网环境下的设备状态监测系统，以提升设备运行效率并降低维护成本。研究采用多源数据融合技术，结合机器学习算法，实现对设备状态的实时监测与预测。通过部署传感器网络采集设备运行数据，利用边缘计算进行初步处理，并将数据传输至云端进行深度分析。实验结果表明，该系统能够准确识别设备异常状态，预测故障发生时间，平均准确率达到95%以上。相较于传统监测方法，本系统具有更高的实时性和准确性，显著提升了设备的可用性和生产效率。

关键词：工业物联网设备状态监测多源数据融合机器学习

Abstract

With the rapid development of industrial Internet of Things technology, the importance of equipment condition monitoring system in industrial production has become increasingly prominent. The purpose of this study is to build an efficient and reliable equipment condition monitoring system in the industrial Internet of Things environment to improve equipment operation efficiency and reduce maintenance costs. In this paper, multi-source data fusion technology and machine learning algorithm are used to realize real-time monitoring and prediction of equipment status. A network of sensors is deployed to collect device operation data, use edge computing for initial processing, and transmit the data to the cloud for in-depth analysis. The experimental results show that the system can accurately identify the abnormal state of the equipment and predict the time of failure with an average accuracy of more than 95%. Compared with the traditional monitoring method, the system has higher real-time and accuracy, and significantly improves the availability and production efficiency of the equipment.

Keywords: Industrial Internet of Things; Equipment condition monitoring; Multi-source data fusion; Machine learning

1 绪论 1

1.1 工业物联网设备监测的研究背景 1

1.2 设备状态监测系统研究现状 1

2 工业物联网环境下的监测系统架构 1

2.1 工业物联网监测系统的组成要素 1

2.2 设备状态数据采集与传输机制 2

2.3 分布式数据处理架构设计 2

3 设备状态监测的关键技术研究 3

3.1 多源异构数据融合方法 3

3.2 基于深度学习的故障诊断模型 4

3.3 实时监测与预警机制优化 4