基于属性可靠度置信规则库的轴承故障诊断研究  

Research on Bearing Fault Diagnosis Based on Belief Rule Base with Attribute Reliability

作　　者：王铎 于延[1] 贺维 WANG Duo;YU Yan;HE Wei(College of Computer Science and Information Engineering,Harbin Normal University,Harbin 150025,China)

机构地区：[1]哈尔滨师范大学计算机科学与信息工程学院,黑龙江哈尔滨150025

出　　处：《软件导刊》2024年第3期94-98,共5页Software Guide

基　　金：教育部产学合作协同育人项目(220601311200013);黑龙江省社会科学基金项目(21GLC189);黑龙江省外国专家项目(GZ20220131);黑龙江省高等教育教学改革研究项目(SJGY20220350,SJGY20210456)。

摘　　要：轴承故障诊断是旋转器械健康管理中的一个关键问题。然而,在工程实践中,轴承的观测数据可能会受到一些干扰因素的影响,包括传感器质量和环境中的噪声等。在传统置信规则库(BRB)中,其模型推理假定输入数据完全可靠,但不可靠的观测数据会使BRB精度降低。具有属性可靠度的置信规则库模型(BRB-r)提供了一种建模框架和分析方法,是一个能够聚合不可靠定量数据和专家知识的系统。为提高轴承故障诊断精度,提出一种基于BRBr的轴承故障诊断模型。首先,基于统计方法计算属性可靠度;然后,使用证据推理作为模型的推理机;最后,采用投影协方差矩阵自适应进化策略(P-CMA-ES)对模型进行参数优化。验证实验结果表明,BRB-r在一定程度上能够消除观测数据中不确定性信息的影响,并对不可靠数据进行有效处理,具备良好的诊断效果。Bearing fault diagnosis is a key issue in the health management of rotating instruments.However,in engineering practice,the ob⁃servation data of bearings may be affected by some interference factors,including sensor quality and environmental noise.In traditional belief rule bases(BRB),the model inference assumes that the input data is completely reliable,but unreliable observation data can reduce the accu⁃racy of BRB.The Belief Rule Base Model with Attribute Reliability(BRB-r)provides a modeling framework and analysis method,and is an expert system that can aggregate unreliable quantitative data and expert knowledge.To improve the accuracy of bearing fault diagnosis,a new bearing fault diagnosis model based on BRB-r is proposed.Firstly,calculate the reliability of attributes based on statistical methods;Then,use evidence reasoning as the inference engine of the model;Finally,the projection covariance matrix adaptive evolution strategy(P-CMAES)is used to optimize the model parameters.The verification experiment results show that BRB-r can to some extent eliminate the influence of uncertain information in observation data and effectively process unreliable data,with good diagnostic performance.

关 键 词：故障诊断 置信规则库 属性可靠度 证据推理 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]