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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

摘要： Rotating machine fault signal extraction becomes increasingly important in practical engineering applications. However, fault signals with low signal-to-noise ratios (SNRs) are difficult to extract, especially at the early stage of fault diagnosis. In this paper, 2D line-defect phononic crystals (PCs) consisting of periodic acrylic tubes with slit are proposed for weak signal detection. The defect band, namely, the formed resonance band of line-defect PCs enables the incident acoustic wave at the resonance frequency to be trapped and enhanced at the resonance cavity. The noise can be filtered by the band gap. As a result, fault signals with high SNRs can be obtained for fault feature extraction. The effectiveness of weak harmonic and periodic impulse signal detection via line-defect PCs are investigated in numerical and experimental studies. All the numerical and experimental results indicate that line-defect PCs can be well used for extracting weak harmonic and periodic impulse signals. This work will provide potential for extracting weak signals in many practical engineering applications.

关键词： phononic crystals line-defect fault signal extraction acoustic enhancement

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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

摘要：

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

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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

《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

摘要：

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

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小波尺度谱在AE信号特征提取中的应用

肖思文,廖传军,李学军

《中国工程科学》 2008年第10卷第11期页码 69-75

摘要：

通过分析典型声发射信号及其特征提取，将小波尺度谱引入到声发射故障诊断领域，首次提出了声发射信号的小波尺度谱分析法。给出了小波基函数及其参数的选取，克服了声发射信号小波尺度谱的时、频分辨率不能同时达到最好的缺陷。将小波尺度谱用于声发射检测的滚动轴承损伤类型及部件的识别，诊断结果十分直观、清晰、准确。仿真分析和实验研究均表明小波尺度谱能有效应用于基于声发射技术的状态监测与故障诊断。

关键词：小波尺度谱声发射特征提取故障诊断滚动轴承

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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

摘要：

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

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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

摘要： Gearbox fault diagnosis based on vibration sensing has drawn much attention for a long time. For highly integrated complicated mechanical systems, the intercoupling of structure transfer paths results in a great reduction or even change of signal characteristics during the process of original vibration transmission. Therefore, using gearbox housing vibration signal to identify gear meshing excitation signal is of great significance to eliminate the influence of structure transfer paths, but accompanied by huge scientific challenges. This paper establishes an analytical mathematical description of the whole transfer process from gear meshing excitation to housing vibration. The gear meshing stiffness (GMS) identification approach is proposed by using housing vibration signals for two stages of inversion based on the mathematical description. Specifically, the linear system equations of transfer path analysis are first inverted to identify the bearing dynamic forces. Then the dynamic differential equations are inverted to identify the GMS. Numerical simulation and experimental results demonstrate the proposed method can realize gear fault diagnosis better than the original housing vibration signal and has the potential to be generalized to other speeds and loads. Some interesting properties are discovered in the identified GMS spectra, and the results also validate the rationality of using meshing stiffness to describe the actual gear meshing process. The identified GMS has a clear physical meaning and is thus very useful for fault diagnosis of the complicated equipment.

关键词： gearbox fault diagnosis meshing stiffness identification transfer path signal processing

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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

《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

摘要：

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

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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

摘要： A blind identification method was developed for the threshold auto-regressive (TAR) model. The method had good identification accuracy and rapid convergence, especially for higher order systems. The proposed method was then combined with the hidden Markov model (HMM) to determine the auto-regressive (AR) coefficients for each interval used for feature extraction, with the HMM as a classifier. The fault diagnoses during the speed-up and speed-down processes for rotating machinery have been successfully completed. The result of the experiment shows that the proposed method is practical and effective.

关键词： speed-up classifier practical extraction experiment

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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

摘要： To catch symptoms of machine failure as early as possible, one of the most important strategies is to apply more progressive techniques during signal processing. This paper presents a method based on stochastic resonance (SR) to detect weak fault signal. First, a discrete model of a bistable system that can demonstrate SR is researched, and the stability condition for controlling the selection of model parameters of the discrete model and guarantee the solving convergence are established. Then, the frequency range of the weak signals that the SR model can detect is extended through a type of normalized scale transformation. Finally, the method is applied to extract the weak characteristic component from heavy noise to indicate the little crack fault in a bearing outer circle.

关键词： extended stochastic resonance (SR) stability analysis of SR scale transform weak signal detection incipient fault detection envelope analysis

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自适应多小波基函数构造与机械故障诊断应用研究

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

摘要：

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。

关键词：机械故障诊断内积变换原理自适应基函数多小波降噪故障特征提取

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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

摘要：缆系海底观测网可实现长时间、实时、原位海洋在线观测，在海洋观测领域扮演重要角色。可靠的海缆切换方法对于建立永久、可靠、鲁棒性高的海底观测网是必要的。对比已有海缆切换方法优缺点，针对海底观测网的网络组态提出一种新颖的海缆切换方法。无需配置额外通讯路由，借助已有电力传输缆，传输基于特定序列的可编程电压信号，实现水下分支器与陆地岸基站的通讯。建立系统仿真模型，分析电压信号最大数据位频率，确保准确识别控制命令。最后，在实验室环境下建立基于所提切换方法的水下分支器样机，验证该方法的功能及可靠性。

关键词：缆系海底观测网；海缆切换及故障隔离；可编程电压信号；最大数据位频率

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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

摘要： Entity and relation extraction is an indispensable part of domain knowledge graph construction, which can serve relevant knowledge needs in a specific domain, such as providing support for product research, sales, risk control, and domain hotspot analysis. The existing entity and relation extraction methods that depend on pretrained models have shown promising performance on open datasets. However, the performance of these methods degrades when they face domain-specific datasets. Entity extraction models treat characters as basic semantic units while ignoring known character dependency in specific domains. Relation extraction is based on the hypothesis that the relations hidden in sentences are unified, thereby neglecting that relations may be diverse in different entity tuples. To address the problems above, this paper first introduced prior knowledge composed of domain dictionaries to enhance characters’ dependence. Second, domain rules were built to eliminate noise in entity relations and promote potential entity relation extraction. Finally, experiments were designed to verify the effectiveness of our proposed methods. Experimental results on two domains, including laser industry and unmanned ship, showed the superiority of our methods. The F1 value on laser industry entity, unmanned ship entity, laser industry relation, and unmanned ship relation datasets is improved by +1%, +6%, +2%, and +1%, respectively. In addition, the extraction accuracy of entity relation triplet reaches 83% and 76% on laser industry entity pair and unmanned ship entity pair datasets, respectively.

关键词： entity extraction relation extraction prior knowledge domain rule

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基于经验模式分解的旋转机械振动信号降噪处理

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

摘要：

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。

关键词：故障诊断经验模式分解旋转机械滤波

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循环双谱及在周期平稳类故障中的应用

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

摘要：

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。

关键词：周期平稳循环双谱滞后积

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超声血流的无创伤检测和医学信号的特征提取

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

摘要：

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。

关键词：超声血流医学信号无创伤检测特征提取

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标题作者时间类型操作

Acoustic fault signal extraction via the line-defect phononic crystals

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

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

小波尺度谱在AE信号特征提取中的应用

肖思文,廖传军,李学军

期刊论文

Weak characteristic information extraction from early fault of wind turbine generator gearbox

Xiaoli XU, Xiuli LIU

期刊论文

Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals

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