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Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG
《机械工程前沿(英文)》 2021年 第16卷 第2期 页码 340-352 doi: 10.1007/s11465-021-0629-3
关键词: fault intelligent diagnosis deep learning deep convolutional neural network high-dimensional samples
Intelligent fault diagnostic system based on RBR for the gearbox of rolling mills
Lixin GAO, Lijuan WU, Yan WANG, Houpei WEI, Hui YE
《机械工程前沿(英文)》 2010年 第5卷 第4期 页码 483-490 doi: 10.1007/s11465-010-0118-6
关键词: rule-based reasoning fault diagnosis intelligent system gear box
Basic research on machinery fault diagnostics: Past, present, and future trends
Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN
《机械工程前沿(英文)》 2018年 第13卷 第2期 页码 264-291 doi: 10.1007/s11465-018-0472-3
Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.
关键词: fault diagnosis fault mechanism feature extraction signal processing intelligent diagnostics
Machine learning for fault diagnosis of high-speed train traction systems: A review
《工程管理前沿(英文)》 doi: 10.1007/s42524-023-0256-2
关键词: high-speed train traction systems machine learning fault diagnosis
《机械工程前沿(英文)》 2021年 第16卷 第4期 页码 814-828 doi: 10.1007/s11465-021-0650-6
关键词: bearing cross-severity fault diagnosis hierarchical fault diagnosis convolutional neural network decision tree
Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals
《机械工程前沿(英文)》 2022年 第17卷 第4期 doi: 10.1007/s11465-022-0713-3
关键词: gearbox fault diagnosis meshing stiffness identification transfer path signal processing
New method of fault diagnosis of rotating machinery based on distance of information entropy
Houjun SU, Tielin SHI, Fei CHEN, Shuhong HUANG
《机械工程前沿(英文)》 2011年 第6卷 第2期 页码 249-253 doi: 10.1007/s11465-011-0124-3
This paper introduces the basic conception of information fusion and some fusion diagnosis methods commonly used nowadays in rotating machinery. From the thought of the information fusion, a new quantitative feature index monitoring and diagnosing the vibration fault of rotating machinery, which is called distance of information entropy, is put forward on the basis of the singular spectrum entropy in time domain, power spectrum entropy in frequency domain, wavelet energy spectrum entropy, and wavelet space feature entropy in time-frequency domain. The mathematic deduction suggests that the conception of distance of information entropy is accordant with the maximum subordination principle in the fuzzy theory. Through calculation it has been proved that this method can effectively distinguish different fault types. Then, the accuracy of rotor fault diagnosis can be improved through the curve chart of the distance of information entropy at multi-speed.
关键词: rotating machinery information fusion fault diagnosis Information entropy distance of the information entropy
Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning
《机械工程前沿(英文)》 2021年 第16卷 第4期 页码 829-839 doi: 10.1007/s11465-021-0652-4
关键词: imbalanced fault diagnosis graph feature learning rotating machinery autoencoder
《机械工程前沿(英文)》 2022年 第17卷 第2期 doi: 10.1007/s11465-022-0673-7
关键词: deep reinforcement learning hyper parameter optimization convolutional neural network fault diagnosis
Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network
《机械工程前沿(英文)》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0692-4
关键词: axial piston pump fault diagnosis convolutional neural network multi-sensor data fusion
于文虎,宋斌
《中国工程科学》 2001年 第3卷 第1期 页码 44-50
文章回顾总结了国内外火电机组振动故障诊断的发展状况,指出了目前振动故障诊断研究存在的问题及确定故障诊断知识范围、进行机组轴系传递特性研究的重要性,对大型火电机组振动及性能远程诊断系统进行了介绍,最后指出了振动故障诊断的发展方向。
Intelligent diagnosis methods for plant machinery
Huaqing WANG, Peng CHEN, Shuming WANG,
《机械工程前沿(英文)》 2010年 第5卷 第1期 页码 118-124 doi: 10.1007/s11465-009-0084-z
关键词: intelligent diagnosis neural network fuzzy algorithm adaptive filtering plant machinery
高金吉
《中国工程科学》 2001年 第3卷 第9期 页码 41-47
应用现代信息技术和人工智能实施设备诊断工程,逐步实现状态维修和预知维修,是大型流程工业企业降低生产成本的重要途径之一。概要介绍为实现这一目标所开发的机电装备实时监测网络和人工智能诊断技术。简要介绍了基于Ethernet和FDDI开发、应用于石化企业的机、泵群实时监测网络;首次提出了黑灰白集合筛选法,在一次原因分析法和故障机理及其识别特征研究基础上,应用此方法开发的基于黑灰白集合筛选法的机械故障诊断专家系统,用于工程实践取得了满意的结果。
Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature
《机械工程前沿(英文)》 2023年 第18卷 第1期 doi: 10.1007/s11465-022-0725-z
关键词: higher order energy operator fault diagnosis manifold learning rolling element bearing information fusion
《机械工程前沿(英文)》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0689-z
关键词: surrogate model gas face seal fault diagnosis nonlinear dynamics tribology
标题 作者 时间 类型 操作
Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples
Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG
期刊论文
Intelligent fault diagnostic system based on RBR for the gearbox of rolling mills
Lixin GAO, Lijuan WU, Yan WANG, Houpei WEI, Hui YE
期刊论文
Basic research on machinery fault diagnostics: Past, present, and future trends
Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN
期刊论文
Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical faultdiagnosis of bearings
期刊论文
Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals
期刊论文
New method of fault diagnosis of rotating machinery based on distance of information entropy
Houjun SU, Tielin SHI, Fei CHEN, Shuhong HUANG
期刊论文
Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning
期刊论文
A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis
期刊论文