文献类型：期刊文献

中文题名：基于BM-MTF的船舶水泵轴承故障特征增强与诊断研究

英文题名：Research on fault feature enhancement and diagnosis of marine water pump bearing based on BM-MTF

作者：廖志强[1,2,3];黄振德[1];宋雪玮[1,2,3];梁观龙[1];贾宝柱[1,2,3]

机构：[1]广东海洋大学船舶与海运学院,广东湛江524088;[2]广东省船舶智能与安全工程技术研究中心,广东湛江524088;[3]省市共建南海海洋牧场智能装备广东省重点实验室,广东湛江524088

年份：2025

卷号：20

期号：2

起止页码：56

中文期刊名：中国舰船研究

外文期刊名：Chinese Journal of Ship Research

收录：北大核心2023、、北大核心

基金：国家自然科学基金资助项目(52201355,52071090,52401418);广东省教育厅重点领域资金项目(2020ZDZX3063);广东海洋大学科研启动经费资助项目(060302132304,060302132101)。

语种：中文

中文关键词：船舶水泵;轴承;故障分析;特征提取;故障特征增强;马尔可夫转移场;ResNet-18网络

外文关键词：marine water pump;bearings;failure analysis;feature extraction;fault feature enhancement;Markov transition field;ResNet-18 network

中文摘要：[目的]针对船舶水泵轴承故障时的振动信号故障特征易被噪声淹没,导致诊断准确率较低的问题,提出一种基于巴特沃斯均值滤波器和马尔可夫转移场(BM-MTF)与ResNet-18网络相结合的轴承故障特征增强与诊断方法。[方法]首先,引入BM滤波器,以强化信号的故障冲击波形,从而抑制噪声干扰、增强故障特征;然后,通过MTF绘制二维图像,以有效可视化并增强信号特征,再将经BM信号滤波后的MTF图像输入ResNet-18网络进行诊断识别;最后,采用西储大学轴承故障公开数据集、实验室轴承故障数据集和船舶水泵轴承故障数据集进行对比验证。[结果]实验对比结果表明,所提BM-MTF方法可以有效提取轴承故障特征,其对3种轴承故障数据集的诊断准确度均达到100%,显著提升了轴承故障准确度。[结论]研究成果可为船舶水泵轴承故障诊断提供参考。

外文摘要：[Objective]Marine water pump bearings operate in complex environments,and the fault features in the acquired signals are easily submerged by noise,resulting in low fault diagnosis accuracy.This paper proposes a Butterworth mean filtering Markov transition field(BM-MTF)technique combined with the Res-Net-18 network to solve the problem.[Methods]First,the BM filter is employed to improve the fault impulse waveform of the signal,suppressing noise interference and amplifying fault characteristics.Then,a twodimensional image is generated through MTF to effectively visualize and enhance the signal characteristics.The MTF images,after BM filtering,are input into the ResNet-18 network for fault diagnosis.Finally,the method is verified using the public bearing fault dataset from Case Western Reserve University,the laboratory bearing fault dataset,and marine water pump bearing fault dataset,with comparisons to other methods.[Results]The proposed method demonstrates a 100% accuracy on three bearing fault datasets.The comparative experiments show that the proposed method can effectively extract fault features and achieves higher recognition accuracy.[Conclusion]This paper presents a novel method for fault diagnosis of marine water pump bearings.