大数据背景下基于轴裂纹的贝叶斯网络故障诊断模型研究

Research on Bayesian Network Fault Diagnosis Model for Shaft Cracks in the Context of Big Data

本研究专注于利用贝叶斯网络的建模方法进行故障诊断,特别关注轴裂纹故障。研究目标是通过比较和应用4种不同的贝叶斯网络模型来确定在轴裂纹故障诊断中最为精确的模型。实验过程中,首先从故障模拟器中收集和整理了轴裂纹故障的相关数据,这些数据包括可能导致轴裂纹的各种参数,如应力水平、材料属性和运行时间等。数据集被划分为训练集和测试集,以便进行模型训练和精度验证。在稳定的实验环境、精准的数据采集设备以及高效的计算机处理系统的支持下,运用4种贝叶斯网络建模方法对训练集进行了模型构建。随后,通过对测试集的验证,对比这4种模型的诊断精度得到最优的诊断模型。这一发现揭示了该方法在故障诊断领域的优势,并为提升工业设备的运行安全性和维护效率提供了实用工具。本研究的创新之处在于将4种不同的贝叶斯网络建模方法应用于轴裂纹故障诊断,并通过实证对比分析确定了最优模型。这一研究成果不仅丰富了故障诊断领域的理论方法,也为实际工程中的轴裂纹故障诊断提供了高效、精准的解决方案。

This research focuses on fault diagnosis using Bayesian network modeling method,with special attention to axial crack fault.The objective of this study is to determine the most accurate model for shaft crack fault diagnosis by comparing and applying 4 different Bayesian network models.During the experiment,the data related to shaft crack fault were collected and sorted out from the fault simulator,including various parameters that may cause shaft crack,such as stress level,material properties and running time.The data set is divided into a training set and a test set for model training and accuracy verification.With the support of stable experimental environment,accurate data acquisition equipment and efficient computer processing system,four Bayesian network modeling methods were used to model the training set.Then,through the verification of the test set,the diagnostic accuracy of these four models is compared to get the optimal diagnostic model.This discovery reveals the advantages of this method in the field of fault diagnosis,and provides a practical tool for improving the operation safety and maintenance efficiency of industrial equipment.The innovation of this study lies in the application of 4 different Bayesian network modeling methods to shaft crack fault diagnosis,and the optimal model is determined through empirical comparative analysis.The research results not only enrich the theoretical methods in the field of fault diagnosis,but also provide an efficient and accurate solution for shaft crack fault diagnosis in practical engineering.