

浏览全部资源
扫码关注微信
"大数据"时代给机械设备智能诊断带来了数据总量大、产生速度快、形式多、价值密度低等新挑战
传统智能故障诊断"人工特征提取+模式识别"的模式已然不能满足发展需求。本文分析了机械大数据的特性对故障诊断结果的影响
详述了堆叠自编码网络(SAE)、卷积神经网络(CNN)、深度置信网络(DBN)、循环神经网络(RNN)4个基本框架和其他深度学习模型在故障诊断领域
尤其是复杂机械数据的特征学习和各种机械设备健康监控任务的目标预测等相关研究。分析了不同模型的利弊和适应问题:SAE与DBN属于无监督学习模型
对数据要求较低
具有强大的特征提取能力
但性能难以保障;CNN在高维数据处理上优势明显
但训练迭代次数较多;RNN可以处理变化的时序数据。文中分析指出机械大数据下深度学习存在的问题:包括机械数据不平衡、来源分散;应用模式简单
缺乏对网络本身性能的分析;机械式引进较多
缺少适应性改造;学习处于"黑箱"阶段
无法解释等。最后
讨论了应对问题的有效措施并对深度学习未来的发展趋势进行展望。
杨杰.基于稀疏高斯伯努利受限玻尔兹曼机的故障分类[D].浙江大学,2018(08).
陈曦.基于高斯伯努利受限玻尔兹曼机的过程监测研究[D].浙江大学,2016(08).
罗金,童靳于,郑近德,潘海洋,潘紫微.基于EEMD和堆叠稀疏自编码的滚动轴承故障诊断方法[J].噪声与振动控制,2020(02).
宫文峰,陈辉,张泽辉,张美玲,管聪,王鑫.基于改进卷积神经网络的滚动轴承智能故障诊断研究[J].振动工程学报,2020(02).
周奇才,沈鹤鸿,赵炯,刘星辰.基于改进堆叠式循环神经网络的轴承故障诊断[J].同济大学学报(自然科学版),2019(10).
陈保家,刘浩涛,徐超,陈法法,肖文荣,赵春华.深度置信网络在齿轮故障诊断中的应用[J].中国机械工程,2019(02).
张西宁,向宙,夏心锐,李立帆.堆叠自编码网络性能优化及其在滚动轴承故障诊断中的应用[J].西安交通大学学报,2018(10).
张西宁,向宙,唐春华.一种深度卷积自编码网络及其在滚动轴承故障诊断中的应用[J].西安交通大学学报,2018(07).
中国工程院周济院长关于“新一代智能制造——新一轮工业革命的核心驱动力”的主题报告[J].起重运输机械,2018(01).
侯文擎,叶鸣,李巍华.基于改进堆叠降噪自编码的滚动轴承故障分类[J].机械工程学报,2018(07).
贾京龙,余涛,吴子杰,程小华.基于卷积神经网络的变压器故障诊断方法[J].电测与仪表,2017(13).
刘浩,熊炘,王小静,郭家宇,沈杰希.基于自组织映射与受限玻尔兹曼机的滚动轴承健康评估[J].机械传动,2017(06).
李敬微,顾晓辉,曹蕾,雷刚,杭发贵.基于包络谱分析和高斯受限玻尔兹曼机的滚动轴承故障诊断方法[J].机械研究与应用,2016(02).
雷亚国,贾峰,周昕,林京.基于深度学习理论的机械装备大数据健康监测方法[J].机械工程学报,2015(21).
刘智慧,张泉灵.大数据技术研究综述[J].浙江大学学报(工学版),2014(06).
孟小峰,慈祥.大数据管理:概念、技术与挑战[J].计算机研究与发展,2013(01).
李国杰,程学旗.大数据研究:未来科技及经济社会发展的重大战略领域——大数据的研究现状与科学思考[J].中国科学院院刊,2012(06).
郎杨琴,孔丽华.美国发布“大数据的研究和发展计划”[J].科研信息化技术与应用,2012(02).
王晓霞,马良玉,王兵树,王涛.进化Elman神经网络在实时数据预测中的应用[J].电力自动化设备,2011(12).
Bin Yang,Yaguo Lei,Feng Jia,Saibo Xing.An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings[J].Mechanical Systems and Signal Processing,2019.
Bin Zhang,Shaohui Zhang,Weihua Li.Bearing performance degradation assessment using long short-term memory recurrent network[J].Computers in Industry,2019.
Siyu Shao,Pu Wang,Ruqiang Yan.Generative adversarial networks for data augmentation in machine fault diagnosis[J].Computers in Industry,2019.
Rui Zhao,Ruqiang Yan,Zhenghua Chen,Kezhi Mao,Peng Wang,Robert X. Gao.Deep learning and its applications to machine health monitoring[J].Mechanical Systems and Signal Processing,2019.
Shuhui Wang,Jiawei Xiang,Yongteng Zhong,Hesheng Tang.A data indicator-based deep belief networks to detect multiple faults in axial piston pumps[J].Mechanical Systems and Signal Processing,2018.
Fan Xu,W. Peter Tse,Yiu Lun Tse.Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath-Geva clustering algorithm without principal component analysis and data label[J].Applied Soft Computing Journal,2018.
Feng Jia,Yaguo Lei,Na Lu,Saibo Xing.Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization[J].Mechanical Systems and Signal Processing,2018.
Ignacio Cordón,Salvador García,Alberto Fernández,Francisco Herrera.imbalance: Oversampling Algorithms for Imbalanced Classification in R[J].Knowledge-Based Systems,2018.
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China,Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China,Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China,Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.IMCStacking: Cost-sensitive stacking learning with feature inverse mapping for imbalanced problems[J].Knowledge-Based Systems,2018.
Zirui Wang,Jun Wang,Youren Wang.An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition[J].Neurocomputing,2018.
Lin Xu,Maoyong Cao,Baoye Song,Jiansheng Zhang,Yurong Liu,Fuad E. Alsaadi.Open-circuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network[J].Neurocomputing,2018.
Han Liu,Jianzhong Zhou,Yang Zheng,Wei Jiang,Yuncheng Zhang.Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders[J].ISA Transactions,2018.
Fenglian Li,Xueying Zhang,Xiqian Zhang,Chunlei Du,Yue Xu,Yu-Chu Tian.Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets[J].Information Sciences,2018.
Osama Abdeljaber,Onur Avci,Serkan Kiranyaz,Moncef Gabbouj,Daniel J. Inman.Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks[J].Journal of Sound and Vibration,2017.
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton.ImageNet classification with deep convolutional neural networks[J].Communications of the ACM,2017.
Shan Pang,Xinyi Yang,Xiaofeng Zhang,Kenneth M. Sobel.Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure[J].International Journal of Aerospace Engineering,2016.
Feng Jia,Yaguo Lei,Jing Lin,Xin Zhou,Na Lu.Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J].Mechanical Systems and Signal Processing,2016.
Shaheryar Ahmad,Yin Xu Cheng,Hao Hong Wei,Ali Hazrat,Iqbal Khalid.A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants[J].Science and Technology of Nuclear Installations,2016.
Haidong Shao,Hongkai Jiang,Xun Zhang,Maogui Niu.Rolling bearing fault diagnosis using an optimization deep belief network[J].Measurement Science and Technology,2015.
Chuan Li,René-Vinicio Sanchez,Grover Zurita,Mariela Cerrada,Diego Cabrera,Rafael E. Vásquez.Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis[J].Neurocomputing,2015.
M. Demetgul,K. Yildiz,S. Taskin,I.N. Tansel,O. Yazicioglu.Fault diagnosis on material handling system using feature selection and data mining techniques[J].Measurement,2014.
Van Tung Tran,Faisal AlThobiani,Andrew Ball.An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks[J].Expert Systems With Applications,2014.
0
Views
3
下载量
24
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621