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《能源前沿(英文)》 2023年 第17卷 第4期 页码 527-544 doi: 10.1007/s11708-023-0880-x
关键词: fault detection unary classification self-supervised representation learning multivariate nonlinear time series
Multiple fault separation and detection by joint subspace learning for the health assessment of wind
Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN
《机械工程前沿(英文)》 2017年 第12卷 第3期 页码 333-347 doi: 10.1007/s11465-017-0435-0
The gearbox of a wind turbine (WT) has dominant failure rates and highest downtime loss among all WT subsystems. Thus, gearbox health assessment for maintenance cost reduction is of paramount importance. The concurrence of multiple faults in gearbox components is a common phenomenon due to fault induction mechanism. This problem should be considered before planning to replace the components of the WT gearbox. Therefore, the key fault patterns should be reliably identified from noisy observation data for the development of an effective maintenance strategy. However, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriate division of fault information in order to satisfy various rigorous decomposition principles or statistical assumptions, such as the smooth envelope principle of ensemble empirical mode decomposition and the mutual independence assumption of independent component analysis. Thus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) technique to construct different subspaces adaptively for different fault patterns. Its main advantage is its capability to learn multiple fault subspaces directly from the observation signal itself. It can also sparsely concentrate the feature information into a few dominant subspace coefficients. Furthermore, it can eliminate noise by simply performing coefficient shrinkage operations. Consequently, multiple fault patterns are reliably identified by utilizing the maximum fault information criterion. The superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigated and verified by the analysis of a data set of a 750 kW WT gearbox. Results show that JSL-MFD is superior to a state-of-the-art technique in detecting hidden fault patterns and enhancing detection accuracy.
关键词: joint subspace learning multiple fault diagnosis sparse decomposition theory coupling feature separation wind turbine gearbox
杨天社,李怀祖,曹雨平
《中国工程科学》 2003年 第5卷 第2期 页码 68-74
推理理论一般分为确定性推理理论和不确定性推理理论。传统的卫星故障检测和诊断应用的是确定性推理。然而,在卫星故障检测和诊断的实践中,仅使用确定性推理是很难对某些故障进行检测和诊断的,因为这时需要合情推理和容错能力。不确定性推理理论可以满足此要求。目前,航天领域的许多专家和实际工作者正致力于应用不确定性推理理论检测和诊断那些用确定性推理无法检测和诊断的故障。不确定性推理理论包括诸如包含度理论、粗糙集理论、证据推理理论、概率推理理论、模糊推理理论等。笔者研究的卫星故障检测和诊断的三种新方法,分别应用了包含度理论、粗糙集理论和证据推理理论。
黄采伦,余小华,陈安华,张剑
《中国工程科学》 2007年 第9卷 第7期 页码 61-64
列车轮对轴承故障是危及列车运行安全的重要因素之一,对其准确检测是高速重载列车需要解决的关键问题。当轮对轴承出现异常时,其振动信号中的幅值就会出现突变点,据此提出了利用频谱细化技术对轮对轴承的振动加速度信号进行分析的方法。实验结果表明,该方法实现了轮对轴承异常的高精度检测,说明这种方法比常规的方法能更有效地检测出轮对轴承的异常状态。
Extended stochastic resonance (SR) and its applications in weak mechanical signal processing
Niaoqing HU, Min CHEN, Guojun QIN, Lurui XIA, Zhongyin PAN, Zhanhui FENG,
《机械工程前沿(英文)》 2009年 第4卷 第4期 页码 450-461 doi: 10.1007/s11465-009-0072-3
关键词: extended stochastic resonance (SR) stability analysis of SR scale transform weak signal detection incipient fault detection envelope analysis
模型不确定性和执行器故障下的四旋翼飞行器主动容错控制方法 None
Yu-jiang ZHONG, Zhi-xiang LIU, You-min ZHANG, Wei ZHANG, Jun-yi ZUO
《信息与电子工程前沿(英文)》 2019年 第20卷 第1期 页码 95-106 doi: 10.1631/FITEE.1800570
Masoud RANJBARNIA, Milad ZAHERI, Daniel DIAS
《结构与土木工程前沿(英文)》 2020年 第14卷 第4期 页码 998-1011 doi: 10.1007/s11709-020-0621-8
关键词: urban tunnel sprayed concrete reverse fault normal fault finite difference analysis
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《机械工程前沿(英文)》 2014年 第9卷 第2期 页码 130-141 doi: 10.1007/s11465-014-0298-6
Fault diagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform (FFT) and continuous wavelet transform (CWT) of vibration signals produced from a bearing with defects on inner race and rolling element, have been examined at low signal to noise ratio. Both simulated and experimental signals from identical bearings have been considered for the purpose of analysis. The bearings have been modeled as spring-mass-dashpot systems and the simulated signals have been obtained considering transfer functions for the bearing systems subjected to impulsive loads due to the defects. Frequency B spline wavelets have been applied for CWT and a discussion on wavelet selection has been presented for better effectiveness. Results show that use of CWT with the proposed wavelets overcomes the short coming of FFT while processing a noisy vibration signals for defect detection of bearings.
关键词: Fault detection spline wavelet continuous wavelet transform fast Fourier transform
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
Acoustic fault signal extraction via the line-defect phononic crystals
《机械工程前沿(英文)》 2022年 第17卷 第1期 页码 10-10 doi: 10.1007/s11465-021-0666-y
关键词: phononic crystals line-defect fault signal extraction acoustic enhancement
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)》 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
《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
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
标题 作者 时间 类型 操作
Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and
期刊论文
Multiple fault separation and detection by joint subspace learning for the health assessment of wind
Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN
期刊论文
Extended stochastic resonance (SR) and its applications in weak mechanical signal processing
Niaoqing HU, Min CHEN, Guojun QIN, Lurui XIA, Zhongyin PAN, Zhanhui FENG,
期刊论文
模型不确定性和执行器故障下的四旋翼飞行器主动容错控制方法
Yu-jiang ZHONG, Zhi-xiang LIU, You-min ZHANG, Wei ZHANG, Jun-yi ZUO
期刊论文
Three-dimensional finite difference analysis of shallow sprayed concrete tunnels crossing a reverse faultor a normal fault: A parametric study
Masoud RANJBARNIA, Milad ZAHERI, Daniel DIAS
期刊论文
Identification of faults through wavelet transform vis-à-vis fast Fourier transform of noisy vibration signals emanated from defective rolling element bearings
null
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 fault
期刊论文
A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis
期刊论文