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机器学习 3

特征抽取 3

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信号消噪；深度自适应阈值学习网络；多尺度特征融合；调制识别 1

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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

摘要： Existing fault diagnosis methods usually assume that there are balanced training data for every machine health state. However, the collection of fault signals is very difficult and expensive, resulting in the problem of imbalanced training dataset. It will degrade the performance of fault diagnosis methods significantly. To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this paper. Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the SuperGraph. And the edge connections in the graph depend on the relationship between signals. On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating machinery. Comprehensive experiments are conducted on a benchmarking publicized dataset and a practical experimental platform, and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.

关键词： imbalanced fault diagnosis graph feature learning rotating machinery autoencoder

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Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

《能源前沿（英文）》 2020年第14卷第4期页码 817-835 doi: 10.1007/s11708-020-0709-9

摘要： Since gas turbine plays a key role in electricity power generating, the requirements on the safety and reliability of this classical thermal system are becoming gradually strict. With a large amount of renewable energy being integrated into the power grid, the request of deep peak load regulation for satisfying the varying demand of users and maintaining the stability of the whole power grid leads to more unstable working conditions of gas turbines. The startup, shutdown, and load fluctuation are dominating the operating condition of gas turbines. Hence simulating and analyzing the dynamic behavior of the engines under such instable working conditions are important in improving their design, operation, and maintenance. However, conventional dynamic simulation methods based on the physic differential equations is unable to tackle the uncertainty and noise when faced with variant real-world operations. Although data-driven simulating methods, to some extent, can mitigate the problem, it is impossible to perform simulations with insufficient data. To tackle the issue, a novel transfer learning framework is proposed to transfer the knowledge from the physics equation domain to the real-world application domain to compensate for the lack of data. A strong dynamic operating data set with steep slope signals is created based on physics equations and then a feature similarity-based learning model with an encoder and a decoder is built and trained to achieve feature adaptive knowledge transferring. The simulation accuracy is significantly increased by 24.6% and the predicting error reduced by 63.6% compared with the baseline model. Moreover, compared with the other classical transfer learning modes, the method proposed has the best simulating performance on field testing data set. Furthermore, the effect study on the hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning knowledge from the physical theory domain or from the real-world operation domain.

关键词： gas turbine dynamic simulation data-driven transfer learning feature similarity

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Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective

《机械工程前沿（英文）》 2023年第18卷第2期 doi: 10.1007/s11465-022-0737-8

摘要： Selective laser melting (SLM) is a unique additive manufacturing (AM) category that can be used to manufacture mechanical parts. It has been widely used in aerospace and automotive using metal or alloy powder. The build orientation is crucial in AM because it affects the as-built part, including its part accuracy, surface roughness, support structure, and build time and cost. A mechanical part is usually composed of multiple surface features. The surface features carry the production and design knowledge, which can be utilized in SLM fabrication. This study proposes a method to determine the build orientation of multi-feature mechanical parts (MFMPs) in SLM. First, the surface features of an MFMP are recognized and grouped for formulating the particular optimization objectives. Second, the estimation models of involved optimization objectives are established, and a set of alternative build orientations (ABOs) is further obtained by many-objective optimization. Lastly, a multi-objective decision making method integrated by the technique for order of preference by similarity to the ideal solution and cosine similarity measure is presented to select an optimal build orientation from those ABOs. The weights of the feature groups and considered objectives are achieved by a fuzzy analytical hierarchy process. Two case studies are reported to validate the proposed method with numerical results, and the effectiveness comparison is presented. Physical manufacturing is conducted to prove the performance of the proposed method. The measured average sampling surface roughness of the most crucial feature of the bracket in the original orientation and the orientations obtained by the weighted sum model and the proposed method are 15.82, 10.84, and 10.62 μm, respectively. The numerical and physical validation results demonstrate that the proposed method is desirable to determine the build orientations of MFMPs with competitive results in SLM.

关键词： selective laser melting (SLM) build orientation determination multi-feature mechanical part (MFMP) fuzzy analytical hierarchy process multi-objective decision making (MODM)

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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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composition differences between processed protein from different animal species by self-organizing feature

Xingfan ZHOU,Zengling YANG,Longjian CHEN,Lujia HAN

《农业科学与工程前沿（英文）》 2016年第3卷第2期页码 171-179 doi: 10.15302/J-FASE-2016095

摘要： Amino acids are the dominant organic components of processed animal proteins, however there has been limited investigation of differences in their composition between various protein sources. Information on these differences will not only be helpful for their further utilization but also provide fundamental information for developing species-specific identification methods. In this study, self-organizing feature maps (SOFM) were used to visualize amino acid composition of fish meal, and meat and bone meal (MBM) produced from poultry, ruminants and swine. SOFM display the similarities and differences in amino acid composition between protein sources and effectively improve data transparency. Amino acid composition was shown to be useful for distinguishing fish meal from MBM due to their large concentration differences between glycine, lysine and proline. However, the amino acid composition of the three MBMs was quite similar. The SOFM results were consistent with those obtained by analysis of variance and principal component analysis but more straightforward. SOFM was shown to have a robust sample linkage capacity and to be able to act as a powerful means to link different sample for further data mining.

关键词： self-organizing feature maps visualization processed animal proteins (PAPs) amino acid

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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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Speech emotion recognitionwith unsupervised feature learning

Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO

《信息与电子工程前沿（英文）》 2015年第16卷第5期页码 358-366 doi: 10.1631/FITEE.1400323

摘要： Emotion-based features are critical for achieving high performance in a speech emotion recognition (SER) system. In general, it is difficult to develop these features due to the ambiguity of the ground-truth. In this paper, we apply several unsupervised feature learning algorithms (including -means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning task-related features by using unlabeled data, to speech emotion recognition. We then evaluate the performance of the proposed approach and present a detailed analysis of the effect of two important factors in the model setup, the content window size and the number of hidden layer nodes. Experimental results show that larger content windows and more hidden nodes contribute to higher performance. We also show that the two-layer network cannot explicitly improve performance compared to a single-layer network.

关键词： Speech emotion recognition Unsupervised feature learning Neural network Affect computing

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

《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

摘要： Intelligent power systems can improve operational efficiency by installing a large number of sensors. Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources. However, the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data. The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms. Moreover, sensor data faults in power systems are dynamically changing and pose another challenge. Therefore, a fault detection method based on self-supervised feature learning was proposed to address the above two challenges. First, self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data. The self-supervised representation learning uses a sequence-based Triplet Loss. The extracted features of large amounts of normal data are then fed into a unary classifier. The proposed method is validated on exhaust gas temperatures (EGTs) of a real-world 9F gas turbine with sudden, progressive, and hybrid faults. A comprehensive comparison study was also conducted with various feature extractors and unary classifiers. The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults. The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms of F1 score.

关键词： fault detection unary classification self-supervised representation learning multivariate nonlinear time series

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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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微阵列数据集的特征选择技术：综合评述、分类和未来方向 Review

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI

《信息与电子工程前沿（英文）》 2022年第23卷第10期页码 1451-1478 doi: 10.1631/FITEE.2100569

摘要：

为获得最佳结果，从微阵列数据集中检索相关特征已成为特征选择（FS）技术的研究热点。本综述旨在全面阐述各种最新特征选择技术，同时介绍了基于微阵列数据集的处理多类分类问题的技术以及提高学习算法性能的不同方法。我们试图理解和解决数据集不平衡问题，以证实研究人员在微阵列数据集上的工作。对文献的分析为理解和强调在通过各种特征选择技术寻找最佳特征子集时存在的众多挑战和问题铺平了道路。同时提供了一个案例说明该方法的实施过程，该方法使用3个微阵列癌症数据集评估一些包装方法和混合方法的分类精度和收敛能力，以确认最优特征子集。

关键词：特征选择；高维；学习技术；微阵列数据集

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基于两级层次特征学习的图像分类方法 Article

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

《信息与电子工程前沿（英文）》 2016年第17卷第9期页码 897-906 doi: 10.1631/FITEE.1500346

摘要：概要：在图像分类任务中，不同类别之间的相似度是不同的，样本经常被误分到相似度较高的类别中。为了区分高度相似类别中的样本，需要更加具体的图像特征，以便于分类器能够提高分类性能。本文提出了一种新颖、有效的基于深度卷积神经网络的两级层次特征学习框架。首先，不同层次的深度特征抽取器使用迁移学习方法进行训练。然后，从全部类别中抽取的通用特征和从高度相似类别中抽取的具体特征被融合成一个特征向量，并将其输入线性分类器进行分类。最后，基于Caltech-256、Oxford Flower-102和Tasmania Coral Point Count三个图像数据集的实验证明，通过两级层次特征学习的深度特征的表达能力十分强大，与传统的扁平多分类方法相比，我们提出的方法能有效的提高分类精度。

关键词：迁移学习；特征学习；深度卷积神经网络；层次分类；谱聚类

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Wavelet design for extracting weak fault feature based on lifting scheme

JIANG Hong-kai, WANG Zhong-sheng, HE Zheng-jia

《机械工程前沿（英文）》 2006年第1卷第2期页码 199-203 doi: 10.1007/s11465-006-0009-z

摘要： Weak fault features of mechanical signals are usually immersed in noisy signals. A new wavelet method based on lifting scheme to match weak fault characteristics is proposed. In this method, an initial set of finite biorthogonal filters is modified by a lifting and dual lifting procedure alternately, and different lifting operators and dual lifting operators are obtained. The properties of the initial wavelet is improved, and the new wavelet with particular properties is designed. Simulation and engineering results confirm that the proposed method is better than other wavelet methods for extracting weak fault feature. Modulus maxima of the detail signal in every operation cycle are extracted, the position and time that weak signal singularity occurs are clearly found, and slight rub-impact fault caused by axis misalignment and rotor imbalance of a heavy oil catalytic cracking set are desirably extracted. extracted.

关键词： misalignment imbalance particular position mechanical

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计算摄像学专题概述 Editorial

Qiong-hai DAI

《信息与电子工程前沿（英文）》 2017年第18卷第9期页码 1205-1206 doi: 10.1631/FITEE.1730000

摘要：计算摄像学是为了突破传统成像技术局限而诞生的新兴领域，在研制新型民用相机和科学观测设备方面均有巨大潜力。过去十余年，计算摄像学在计算机视觉、图形学、光学以及信号处理等多学科交叉领域开启了新的前沿，学术界和工业界共同见证了此领域的一系列创新和重大进展。可以说，该领域充满机遇和挑战。为此，我们出版此计算摄像学专题，以推动此领域的研究。依据视觉信号的维度，我们将计算摄像学研究分为空间结构成像、多光谱采集、相位成像以及瞬态信息记录等。计算摄像学研究也受益于光电科技的发展。为促进读者对此领域最新进展的全面了解，此专题包括8篇邀请文章，其中7篇综述——包括1篇该领域总体概述和6篇关于视觉信号不同维度计算成像方法进展的调查——以及1篇关于片上光互连近期进展的研究论文。

关键词： None

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动力气垫地效翼船的设计特点及其发展前景

恽良,邬成杰,谢佑农,彭桂华

《中国工程科学》 2000年第2卷第3期页码 67-72

摘要：

文章简要地阐述了动力气垫地效翼船（又名两栖地效翼船）的设计特点及其在中国的发展过程。同时也指出其在军、民方面可能的发展前景，即设想发展一种同时具有两栖性、高速性、耐波性，既可在地效区内高速稳定航行，也可飞出地效区作空中跳跃、机动，还可作软着陆的两栖地效飞行器（Amphibious WIG Plane）,与俄罗斯最近发展的地效飞行器（Экранолëт）极为相似。

关键词：动力气垫两栖地效翼船特点前景