滚动轴承故障诊断研究综述

Jiangquan Zang, Yi Sun, Liang Guo, et al. A new bearing fault diagnosis method based on modified convolutional neural networks[J]. Chinese Journal of Aeronautics, 2020, 33: 439-447.

Duy-Tang Hoang,Hee-Jun Kang. A survey on Deep Learning based bearing fault diagnosis[J]. Neurocomputing, 2019, 335: 327-335.

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刘永志. 基于深度迁移学习的滚动轴承故障诊断方法研究[D]. 西南交通大学, 2022

金国强. 基于深度学习的复杂工况下端到端的滚动轴承故障诊断算法研究[D]. 中国科学技术大学, 2020

Rai Akhand, Upadhyay S.H. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings[J]. Tribology International, 2016.

Bodi Cui, ang Weng, Ning Zhang. A feature extraction and machine learning framework for bearing fault diagnosis[J]. Renewable Energy, 2022, 191: 987-997.

刘迎松, 魏志刚, 束海星, 等. 基于参数自适应VMD和MCKD的滚动轴承微弱故障特征提取[J]. 噪声与振动控制, 2023, 43(03): 102-109.

王新刚, 王超, 韩凯忠. 基于优化VMD和MCKD的滚动轴承早期故障诊断方法[J]. 东北大学学报(自然科学版), 2021, 42(03): 373-380, 388.

陈远帆, 李舜酩. 基于高斯混合模型与改进网格搜索法的轴承故障诊断[J]. 重庆理工大学学报(自然科学), 2016, 30(03): 34-39.

刘敏, 叶艳媛, 杨清清, 等. 基于CEEMDAN和倒频谱方法的圆锥滚子轴承振动信号分析[J]. 机电工程技术, 2023, 52(08): 165-170.

江志农, 张永申, 冯坤, 等. 基于特征增强倒频谱分析的齿轮故障诊断方法[J]. 机械传动, 2019, 43(10): 13-17, 55.

赵克钦, 程峰, 杨世飞. 变转速下对数平方包络谱在滚动轴承故障诊断中的应用[J]. 噪声与振动控制, 2023, 43(02): 132-138.

王茜, 田慕琴, 宋建成, 等. 基于经验小波变换的振动信号特征量提取[J]. 振动与冲击, 2021, 40(16): 261-266.

杨健, 张永平. 基于小波包和聚类算法的滚动轴承故障检测研究[J]. 盐城工学院学报(自然科学版), 2023, 36(01): 67-73.

陈代俊, 陈里里, 董绍江. 基于VMD-CWT-CNN的滚动轴承故障诊断[J]. 机械强度, 2023, 45(06): 1280-1285.

李军星, 徐行, 贾现召, 等. 基于EEMD与CNN-BiLSTM噪声环境下滚动轴承故障预测方法研究[J]. 轴承, 2023.

黄晓诚, 贺青川, 陈文华. 基于VMD与MLP的电机轴承故障检测方法[J]. 机电工程, 2022, 39(07): 911-918.

孟宗, 吕蒙, 殷娜, 等. 基于改进变分模态分解的滚动轴承故障诊断方法[J]. 计量学报, 2020, 41(06): 717-723.

刘飞, 陈仁文, 邢凯玲, 等. 基于迁移学习与深度残差网络的滚动轴承快速故障诊断算法[J]. 振动与冲击, 2022, 41(03): 154-164.

王亚萍, 许迪, 葛江华, 等. 基于SPWVD时频图纹理特征的滚动轴承故障诊断[J]. 振动.测试与诊断, 2017, 37(01): 115-119, 203.

陈司昱. 基于支持向量机的滚动轴承故障特征选择和诊断方法研究[D]. 长春工业大学, 2023

汤天宝, 周志健, 张涛, 等. 基于奇异谱分解和两层支持向量机轴承故障诊断方法[J]. 噪声与振动控制, 2022, 42(01): 100-105.

易静姝. 人工神经网络在滚动轴承故障诊断中的应用与发展[J]. 价值工程, 2019, 38(24): 274-276.

刘斌, 刘佳, 张海鹏. 基于经验模态分析的机床主轴轴承外圈非接触式故障检测方法[J]. 制造技术与机床, 2023, No.727(01): 21-28.

朱兴统. 基于小波包分解和K最近邻算法的轴承故障诊断方法[J]. 装备制造技术, 2020, (02): 24-27, 45.

丁明彬. 基于小波变换和决策树的电机滚动轴承故障诊断[J]. 内燃机与配件, 2023, No.395(23): 54-57.

沙盟. 基于改进随机森林的电机轴承故障诊断研究[D]. 太原科技大学, 2023

Bo Peng, Ying Bi, Bing Xue, et al. A Survey on Fault Diagnosis of Rolling Bearings[J]. Algorithms, 2022, 15: 347.

贾美霞, 韩宝坤, 王金瑞,等. 基于迁移堆栈自编码器的轴承故障诊断方法[J]. 噪声与振动控制, 2021, 41(06): 84-89, 125.

苏靖涵, 张潇. 基于深度迁移自编码器的变工况下滚动轴承故障诊断方法[J]. 计算机测量与控制, 2021, 29(07): 85-90, 99.

龚俊, 张月义, 陈思戢, 等. 基于SWT与改进卷积神经网络的轴承故障诊断[J]. 现代电子技术, 2024, 47(06): 68-74.

李辉, 徐伟烝. 噪声干扰下的CCSD-CNN轴承故障诊断方法[J]. 轴承, 2023, (10): 93-100.

董绍江, 裴雪武, 吴文亮, 等. 基于多层降噪技术及改进卷积神经网络的滚动轴承故障诊断方法[J]. 机械工程学报, 2021, 57(01): 148-156.

刘鹏, 皮骏, 胡超. 基于DBN网络的滚动轴承故障诊断[J]. 组合机床与自动化加工技术, 2024, (01): 140-144.

沈长青, 汤盛浩, 江星星, 等. 独立自适应学习率优化深度信念网络在轴承故障诊断中的应用研究[J]. 机械工程学报, 2019, 55(07): 81-88.

邵良杉, 朱思佳. 基于改进HHO-LSTM的滚动轴承故障诊断研究[J]. 机械强度, 2024, 46(01): 17-23.

郑直, 张华钦, 潘月. 基于改进鲸鱼算法优化LSTM的滚动轴承故障诊断[J]. 振动与冲击, 2021, 40(07): 274-280.

Zhang Shen, Zhang Shibo, Wang Bingnan. Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review[J]. IEEE Access, 2020, 8: 29857-29881.

王鹏, 李丹青, 王恒. 基于改进交替迁移学习的滚动轴承故障诊断算法[J]. 振动与冲击, 2024, 43(05): 239-249.

高丽鹏, 雷文平, 曹亚磊, 等. 深度多模态迁移学习在轴承故障诊断中的研究[J]. 机械设计与制造, 2023: 1-5.

Neupane Dhiraj, Seok Jongwon. Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review[J]. IEEE Access, 2020, 8: 93155-93178.

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