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提升KPCA方法特征抽取效率的算法设计

徐勇,杨静宇,陆建峰

《中国工程科学》 2005年第7卷第10期页码 38-42

摘要：

在PCA基础上发展出的KPCA方法能抽取样本的非线性特征分量。然而, 基于KPCA的特征抽取需计算所有训练样本与待抽取特征的样本间的核函数, 因此, 训练集的大小制约着特征抽取的效率。为了提高效率，假设特征空间中变换轴可由一部分训练样本(节点)线性表出，并设计了改进的KPCA算法(IKPCA)。该算法抽取某样本特征时，只需计算该样本与节点间的核函数即可。实验结果显示，IKPCA在对应较好性能的同时，具有明显的效率上的优势。

关键词： KPCA IKPCA 特征抽取特征空间

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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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Feature extraction from slice data for reverse engineering

ZHANG Yingjie, LU Shangning

《机械工程前沿（英文）》 2007年第2卷第1期页码 25-31 doi: 10.1007/s11465-007-0004-z

摘要： A new approach to feature extraction for slice data points is presented. The reconstruction of objects is performed as follows. First, all contours in each slice are extracted by contour tracing algorithms. Then the data points on the contours are analyzed, and the curve segments of the contours are divided into three categories: straight lines, conic curves and B-spline curves. The curve fitting methods are applied for each curve segment to remove the unwanted points with pre-determined tolerance. Finally, the features, which consist of the object and connection relations among them, are founded by matching the corresponding contours in adjacent slices, and 3D models are reconstructed based on the features. The proposed approach has been implemented in OpenGL, and the feasibility of the proposed method has been verified by several cases.

关键词： feasibility corresponding B-spline pre-determined tolerance extraction

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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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利用两类投影方法进行特征融合的人脸识别

张生亮,徐勇,杨健,杨静宇

《中国工程科学》 2006年第8卷第8期页码 50-55

摘要：

提出了利用两类投影抽取特征、用并行策略融合特征进行人脸识别的新方法。先用一维的基于向量的投影抽取一组特征，再用基于二维的图像投影的方法抽取一组特征，用复向量将样本的两组特征向量组合在一起，在复向量空间分析主分量(CPCA)，抽取人脸图像的鉴别特征。在FERET人脸库上的实验结果表明，该方法的识别性能比用单个特征有10%左右的提高。

关键词：特征融合线性鉴别分析(LDA) 特征抽取人脸识别

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signal analysis based on general parameterized time--frequency transform and its application in the featureextraction 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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基于核空间非线性特征提取的图像质量评价方法 Article

Yong DING,Nan LI,Yang ZHAO,Kai HUANG

《信息与电子工程前沿（英文）》 2016年第17卷第10期页码 1008-1017 doi: 10.1631/FITEE.1500439

摘要：概要：在实现对与人类视觉感知相一致的图像质量的客观评价中，如何提取图像的视觉感知特征至关重要。不同于传统方法中通过线性变换或模型表达图像的方式，本文采用高维空间的一种数学表达来揭示图像的统计特性，通过引入核独立分量分析（kernel independent component analysis, KICA）方法实现非线性转化和图像的高维特征提取。从而提出一种基于非线性特征提取的全参考图像质量评价方法。在LIVE、TID2008和CSIQ等图像质量评价数据库上的实验结果表明，图像的非线性特征更有利于图像内在质量的描述，并且本文提出的方法性能良好，与主观评价较为一致。

关键词：图像质量评价；全参考方法；特征提取；核空间；支持向量回归

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Feature extraction of hyperspectral images for detecting immature green citrus fruit

Yongjun DING, Won Suk LEE, Minzan LI

《农业科学与工程前沿（英文）》 2018年第5卷第4期页码 475-484 doi: 10.15302/J-FASE-2018241

摘要：

At an early immature growth stage of citrus, a hyperspectral camera of 369–1042 nm was employed to acquire 30 hyperspectral images in order to detect immature green fruit within citrus trees under natural illumination conditions. First, successive projections algorithm (SPA) were implemented to select 677, 804, 563, 962, and 405 nm wavebands and to construct multispectral images from the original hyperspectral images for further processing. Then, histogram threshold segmentation using NDVI of 804 and 677 nm was implemented to remove image backgrounds. Three slope parameters, calculated from the pairs 405 and 563 nm, 563 and 677 nm, and 804 and 962 nm were used to construct a classifier to identify the potential citrus fruit. Then, a marker-controlled watershed segmentation based on wavelet transform was applied to obtain potential fruit areas. Finally, a green fruit detection model was constructed according to Grey Level Co-occurrence Matrix (GLCM) texture features of the independent areas. Three supervised classifiers, logistic regression, random forest and support vector machine (SVM) were developed using texture features. The detection accuracies were 79%, 75%, and 86% for the logistic regression, random forest, and SVM models, respectively. The developed algorithm showed a great potential for identifying immature green citrus for an early yield estimation.

关键词： hyperspectral green citrus image processing fruit detection precision agriculture yield mapping

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Xiao-hu MA,Meng YANG,Zhao ZHANG

《信息与电子工程前沿（英文）》 2016年第17卷第3期页码 212-223 doi: 10.1631/FITEE.1500255

摘要： The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features, while many algorithms generally ignore this property. In this paper, we introduce a novel feature extraction method called local uncorrelated local discriminant embedding (LULDE). The proposed approach can be seen as an extension of a local discriminant embedding (LDE) framework in three ways. First, a new local statistical uncorrelated criterion is proposed, which effectively captures the local information of interclass and intraclass. Second, we reconstruct the affinity matrices of an intrinsic graph and a penalty graph, which are mentioned in LDE to enhance the discriminant property. Finally, it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data, which avoids losing some discriminant information. Experimental results on Yale, ORL, Extended Yale B, and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.

关键词： Feature extraction Local discriminant embedding Local uncorrelated criterion Face recognition

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基于判别扩散映射分析的内蕴特征提取方法在刀具磨损评估中的应用 None

Yi-xiang HUANG, Xiao LIU, Cheng-liang LIU, Yan-ming LI

《信息与电子工程前沿（英文）》 2018年第19卷第11期页码 1352-1361 doi: 10.1631/FITEE.1601512

摘要：针对铣削加工刀具磨损评估，提出一种基于判别扩散映射分析的方法。判别扩散映射分析（discriminant diffusion maps analysis，DDMA）用于时频域特征提取融合与维度缩减。通过保持内蕴特征空间的扩散距离，耦合时频域特征和判别内核，提取有效信息。该方法包含3个步骤：(1)信号处理与特征提取；(2)内蕴维度估计；(3)基于扩散距离保持的特征融合实现。将该方法应用于数控加工中心主轴驱动电流信号，评估刀具磨损状态。与常见主成分分析方法相比，该方法能更好保持刀具磨损状态相关的有用内蕴信息，大大降低与刀具磨损相关的特征维度，并可直接应用于大多数工业数控加工中心，方便获取电流信号实际情况。

关键词：刀具状态监测；流形学习；降维；扩散映射分析；内蕴特征提取

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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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机械产品的信息化——面向机械装备的信息技术

屈梁生,胡兆勇

《中国工程科学》 2004年第6卷第11期页码 20-28

摘要：

机械工业是我国的传统工业，机械装备技术直接影响和制约国民经济诸多领域的发展。信息科学是一门迅速发展的前沿学科，已经渗透到国民经济的各个领域。文章从机械产品的角度出发，讨论了如何用信息技术来提升和发展传统的机械产品，提出了机械产品信息化的内涵、特点与实现的步骤；结合作者过去的一些科研实践，重点分析了机械产品信息化过程的信息融合和特征提取两个技术关键；文章最后强调了理论开拓是产品拥有自主知识产权和核心技术的前提。

关键词：信息化机械工业信息融合特征提取

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Entity and relation extraction with rule-guided dictionary as domain knowledge

《工程管理前沿（英文）》 2022年第9卷第4期页码 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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VMMAO-YOLO: an ultra-lightweight and scale-aware detector for real-time defect detection of avionics thermistor wire solder joints

《机械工程前沿（英文）》 2024年第19卷第3期 doi: 10.1007/s11465-024-0793-3

摘要： The quality of the exposed avionics solder joints has a significant impact on the stable operation of the in-orbit spacecrafts. Nevertheless, the previously reported inspection methods for multi-scale solder joint defects generally suffer low accuracy and slow detection speed. Herein, a novel real-time detector VMMAO-YOLO is demonstrated based on variable multi-scale concurrency and multi-depth aggregation network (VMMANet) backbone and “one-stop” global information gather-distribute (OS-GD) module. Combined with infrared thermography technology, it can achieve fast and high-precision detection of both internal and external solder joint defects. Specifically, VMMANet is designed for efficient multi-scale feature extraction, which mainly comprises variable multi-scale feature concurrency (VMC) and multi-depth feature aggregation-alignment (MAA) modules. VMC can extract multi-scale features via multiple fix-sized and deformable convolutions, while MAA can aggregate and align multi-depth features on the same order for feature inference. This allows the low-level features with more spatial details to be transmitted in depth-wise, enabling the deeper network to selectively utilize the preceding inference information. The VMMANet replaces inefficient high-density deep convolution by increasing the width of intermediate feature levels, leading to a salient decline in parameters. The OS-GD is developed for efficacious feature extraction, aggregation and distribution, further enhancing the global information gather and deployment capability of the network. On a self-made solder joint image data set, the VMMAO-YOLO achieves a mean average precision mAP@0.5 of 91.6%, surpassing all the mainstream YOLO-series models. Moreover, the VMMAO-YOLO has a body size of merely 19.3 MB and a detection speed up to 119 frame per second, far superior to the prevalent YOLO-series detectors.

关键词： defect detection of solder joints VMMAO-YOLO ultra-lightweight and high-performance multi-scale feature extraction VMC and MAA modules OS-GD

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统计不相关最佳鉴别矢量集的本质研究

吴小俊,杨静宇,王士同,刘同明,Josef Kittler

《中国工程科学》 2004年第6卷第2期页码 44-47

摘要：

对统计不相关最佳鉴别矢量集的本质进行研究，在基于总体散布矩阵特征分解的基础上，构造了一种白化变换，使得变换后的样本空间中的总体散布矩阵为单位矩阵，这样使得传统的最佳鉴别矢量集算法得到的均是具有统计不相关的最佳鉴别矢量集，从而揭示了统计不相关最佳鉴别变换的本质——白化变换加普通的线性鉴别变换。该方法的最大优点在于所获得的最优鉴别矢量同时具有正交性和统计不相关性。该方法对代数特征抽取具有普遍适用性。用ORL人脸数据库的数值实验，验证了该方法的有效性。

关键词：模式识别特征抽取鉴别分析广义最佳鉴别矢量集人脸识别