检索范围：
排序： 展示方式：
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
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
Peng ZHOU, Zhike PENG, Shiqian CHEN, Yang YANG, Wenming ZHANG
《机械工程前沿（英文）》 2018年 第13卷 第2期 页码 292-300 doi: 10.1007/s11465-017-0443-0
With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time–frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.
关键词： rotary machines condition monitoring fault diagnosis GPTFT SCI
Weak characteristic information extraction from early fault of wind turbine generator gearbox
Xiaoli XU, Xiuli LIU
《机械工程前沿（英文）》 2017年 第12卷 第3期 页码 357-366 doi: 10.1007/s11465-017-0423-4
Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition (SVD)-based denoising may result in loss of useful information. A weak characteristic information extraction based on µ-SVD and local mean decomposition (LMD) is developed to address this problem. The basic principle of the method is as follows: Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and µ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.
关键词： wind turbine generator gearbox µ-singular value decomposition local mean decomposition weak characteristic information extraction early fault warning
Yun KONG, Tianyang WANG, Zheng LI, Fulei CHU
《机械工程前沿（英文）》 2017年 第12卷 第3期 页码 406-419 doi: 10.1007/s11465-017-0419-0
Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics: Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum (SK-TWES) in the paper. Firstly, the spectral kurtosis (SK) and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.
关键词： wind turbine planet gear fault feature extraction spectral kurtosis time wavelet energy spectrum
Blind identification of threshold auto-regressive model for machine fault diagnosis
LI Zhinong, HE Yongyong, CHU Fulei, WU Zhaotong
《机械工程前沿（英文）》 2007年 第2卷 第1期 页码 46-49 doi: 10.1007/s11465-007-0007-9
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
何正嘉,孙海亮,訾艳阳
《中国工程科学》 2011年 第13卷 第10期 页码 83-92
设备在运行中萌生的故障(即早期故障)，特征信息微弱且往往被机械设备运行过程的强噪声所淹没，给故障诊断与预示带来困难，已成为国内外此领域研究的热点和难点。文章深入研究了机械故障动态信号与基函数的内积变换原理；提出了若干自适应多小波基函数构造方法；改进了几种多小波邻域区间和局部阈值降噪方法。利用典型的工程案例分析和阐述了重油催化裂化装置、连铸连轧机组、空分机、电力机车和船载卫星通信地球站传动系统在运行状态下，微弱动态信号的特征增强和复合故障特征提取的工程应用实效。
基于可编程电压信号实现海底观测网海缆切换及故障隔离 None
Zhi-feng ZHANG, Yan-hu CHEN, De-jun LI, Bo JIN, Can-jun YANG, Jun WANG
《信息与电子工程前沿（英文）》 2018年 第19卷 第11期 页码 1328-1339 doi: 10.1631/FITEE.1601843
Entity and relation extraction with rule-guided dictionary as domain knowledge
《工程管理前沿（英文）》 页码 610-622 doi: 10.1007/s42524-022-0226-0
关键词： entity extraction relation extraction prior knowledge domain rule
苏中元,贾民平,许飞云,胡建中
《中国工程科学》 2006年 第8卷 第9期 页码 57-60
论述了未知循环频率的周期平稳信号循环双谱的估计方法；研究了在循环双谱的循环累积量计算中涉及变量的简化存储方法，提出了该变量矩阵是对称阵，通过算法可以三角阵的元素来表述，克服了循环双谱传统估计方法计算量较大的缺陷，提高了运算效率；提出了循环双谱对调相故障信号的分析能力，以及对加性噪声的处理能力，仿真并验证了该方法的有效性，并将其应用于旋转机械状态分析。
杨建文,贾民平,许飞云,胡建中
《中国工程科学》 2005年 第7卷 第8期 页码 66-69
分析了经验模式分解的滤波性能；针对旋转机械振动信号信噪比低及非平稳特性，应用经验模式分解对振动信号进行降噪处理，突出了有用振动信号，为旋转机械在线监测和故障诊断提供了有效的依据；仿真实验及真实数据分析表明，经验模式分解在振动信号降噪处理中是有效的，为振动信号分析提供了新的方法。
王威琪,汪源源,余建国,吴晓峰,刘斌,张羽,陈斯中,仪艳华,邵谦明
《中国工程科学》 2001年 第3卷 第2期 页码 52-64
人体信号是随机性和背景噪声都很强的复杂信号。文章首先研究了利用超声Doppler技术定量检测血流速度的方法，然后将一些现代信息处理中的新理论、新方法引入医学超声的信息处理，为医学超声信息的特征提取提供了新的手段。这些理论包括：分形、数学形态学、数量化、小波变换、极点轨迹和血管传输线模型等。文章最后阐述了利用上述新方法作为技术核心而研制的三套应用系统；肺动脉血液动力学参数的无损估测系统、彩色编码的声谱系统和超声血流定量检测系统。
标题 作者 时间 类型 操作
Basic research on machinery fault diagnostics: Past, present, and future trends
Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN
期刊论文
Non-stationary signal analysis based on general parameterized time--frequency transform and its applicationin the feature extraction of a rotary machine
Peng ZHOU, Zhike PENG, Shiqian CHEN, Yang YANG, Wenming ZHANG
期刊论文
Weak characteristic information extraction from early fault of wind turbine generator gearbox
Xiaoli XU, Xiuli LIU
期刊论文
Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet
Yun KONG, Tianyang WANG, Zheng LI, Fulei CHU
期刊论文
Blind identification of threshold auto-regressive model for machine fault diagnosis
LI Zhinong, HE Yongyong, CHU Fulei, WU Zhaotong
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
基于可编程电压信号实现海底观测网海缆切换及故障隔离
Zhi-feng ZHANG, Yan-hu CHEN, De-jun LI, Bo JIN, Can-jun YANG, Jun WANG
期刊论文
京公网安备 11010502051620号