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An approach for mechanical fault classification based on generalized discriminant analysis

LI Wei-hua, SHI Tie-lin, YANG Shu-zi

《机械工程前沿（英文）》 2006年 第1卷 第3期   页码 292-298 doi: 10.1007/s11465-006-0022-2

摘要： To deal with pattern classification of complicated mechanical faults, an approach to multi-faults classification based on generalized discriminant analysis is presented. Compared with linear discriminant analysis (LDA), generalized discriminant analysis (GDA), one of nonlinear discriminant analysis methods, is more suitable for classifying the linear non-separable problem. The connection and difference between KPCA (Kernel Principal Component Analysis) and GDA is discussed. KPCA is good at detection of machine abnormality while GDA performs well in multi-faults classification based on the collection of historical faults symptoms. When the proposed method is applied to air compressor condition classification and gear fault classification, an excellent performance in complicated multi-faults classification is presented.

关键词： generalized discriminant     non-separable     abnormality     classification     multi-faults classification    

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A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

《机械工程前沿（英文）》 2023年 第18卷 第2期 doi: 10.1007/s11465-022-0736-9

摘要： Recently, advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines. Given the advantage of obtaining accurate diagnosis results, multi-sensor fusion has long been studied in the fault diagnosis field. However, existing studies suffer from two weaknesses. First, the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types. Second, the localization for multi-source faults is seldom investigated, although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable. This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations (MSRs). First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition results. Second, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault sources are therefore determined. The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump. Results show the proposed method’s validity in diagnosing fault types and sources.

关键词： fault recognition     fault localization     multi-sensor relations     network analysis     graph neural network    

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Urban landscape classification using Chinese advanced high-resolution satellite imagery and an object-orientedmulti-variable model

Li-gang MA,Jin-song DENG,Huai YANG,Yang HONG,Ke WANG

《信息与电子工程前沿（英文）》 2015年 第16卷 第3期   页码 238-248 doi: 10.1631/FITEE.1400083

摘要： The Chinese ZY-1 02C satellite is one of the most advanced high-resolution earth observation systems designed for terrestrial resource monitoring. Its capability for comprehensive landscape classification, especially in urban areas, has been under constant study. In view of the limited spectral resolution of the ZY-1 02C satellite (three bands), and the complexity and heterogeneity across urban environments, we attempt to test its performance of urban landscape classification by combining a multivariable model with an object-oriented approach. The multiple variables including spectral reflection, texture, spatial autocorrelation, impervious surface fraction, vegetation, and geometry indexes were first calculated and selected using forward stepwise linear discriminant analysis and applied in the following object-oriented classification process. Comprehensive accuracy assessment which adopts traditional error matrices with stratified random samples and polygon area consistency (PAC) indexes was then conducted to examine the real area agreement between a classified polygon and its references. Results indicated an overall classification accuracy of 92.63% and a kappa statistic of 0.9124. Furthermore, the proposed PAC index showed that more than 82% of all polygons were correctly classified. Misclassification occurred mostly between residential area and barren/farmland. The presented method and the Chinese ZY-1 02C satellite imagery are robust and effective for urban landscape classification.

关键词： ZY-1 02C satellite     Classification     Urban     Multi-variable model    

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A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumerelectrical appliances classification

Chuan Choong YANG, Chit Siang SOH, Vooi Voon YAP

《能源前沿（英文）》 2019年 第13卷 第2期   页码 386-398 doi: 10.1007/s11708-017-0497-z

摘要： The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggregation methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the - trajectory. The classification algorithm performs cropping and image pyramid reduction of the - trajectory plot template images before utilizing the principal component analysis (PCA) and the -nearest neighbor ( -NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through - trajectory-based load signature images by utilizing a multi-stage classification algorithm methodology. The contribution of this paper is in utilizing the “ -value,” the number of closest data points to the nearest neighbor, in the -NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.

关键词： load disaggregation     voltage-current (V-I) trajectory     multi-stage classification algorithm     principal component analysis (PCA)     k-nearest neighbor (k-NN)    

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

《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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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

《结构与土木工程前沿（英文）》 2022年 第16卷 第2期   页码 214-223 doi: 10.1007/s11709-021-0800-2

摘要： In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.

关键词： concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

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Condition monitoring of a wind turbine generator using a standalone wind turbine emulator

Himani,Ratna DAHIYA

《能源前沿（英文）》 2016年 第10卷 第3期   页码 286-297 doi: 10.1007/s11708-016-0419-5

摘要： The intend of this paper is to give a description of the realization of a low-cost wind turbine emulator(WTE) with open source technology from graze required for the condition monitoring to diagnose rotor and stator faults in a wind turbine generator (WTG). The WTE comprises of a 2.5 kW DC motor coupled with a 1 kW squirrel-cage induction machine. This paper provides a detailed overview of the hardware and software used along with the WTE control strategies such as MPPT and pitch control. The emulator reproduces dynamic characteristics both under step variations and arbitrary variation in the wind speed of a typical wind turbine (WT) of a wind energy conversion system (WECS). The usefulness of the setup has been benchmarked with previously verified WT test rigs made at the University of Manchester and Durham University in UK. Considering the fact that the rotor blades and electric subassemblies direct drive WTs are most susceptible to damage in practice, generator winding faults and rotor unbalance have been introduced and investigated using the terminal voltage and generated current. This wind turbine emulator (WTE) can be reconfigured or analyzed for condition monitoring without the need for real WTs.

关键词： condition monitoring (CM)     wind turbine emulator (WTE)     wind turbine generator (WTG)     maximum power point tracking (MPPT)     tip speed ratio (TSR)     rotor faults     stator faults    

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Development of a new method for RMR and Q classification method to optimize support system in tunneling

Asghar RAHMATI,Lohrasb FARAMARZI,Manouchehr SANEI

《结构与土木工程前沿（英文）》 2014年 第8卷 第4期   页码 448-455 doi: 10.1007/s11709-014-0262-x

摘要： Rock mass classification system is very suitable for various engineering design and stability analysis. classification method is confirmed by Japan Highway Public Corporation that this method can figure out either strength or deformability of rock mass, further appropriating the amount of rock bolts, thickness of shotcrete, and size of pitch of steel ribs just after the blasting procedure. Based on these advantages of method, in this study, according to data of five deep and long tunnels in Iran, two equations for estimating the value of method from and classification systems were developed. These equations as a new method were able to optimize the support system for and classification systems. From classification and its application in these case studies, it is pointed out that the method for the design of support systems in underground working is more reliable than the and classification systems.

关键词： JH classification     Q and RMR classification     new method    

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知识推送系统中一种基于多分类径向基神经网络的知识匹配方法 Research Articles

张树有,顾叶,伊国栋,王自立

《信息与电子工程前沿（英文）》 2020年 第21卷 第7期   页码 963-1118 doi: 10.1631/FITEE.1900057

摘要： 聚焦知识匹配领域，开展提高产品设计中知识推送系统性能的探索性研究。传统匹配算法需重复计算，导致响应时间长，准确性也有待提高。本文目标是实现对设计者知识需求的快速响应，并提供优质知识推送服务。在改进之前工作基础上，研究实际操作中增强有限训练集的两种方法：案例特征向量中振荡特征权值和修正案例特征。此外，提出一种多分类径向基神经网络，可从知识库中一次性匹配知识并保证推送结果准确性。使用数控机床中导轨设计的训练集训练和测试该方法，实验结果表明增强训练集有效，本文提出的方法优于其他匹配方法。

关键词： 产品设计；知识推送系统；增强训练集；多分类神经网络；知识匹配    

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Molecular classification and precision therapy of cancer: immune checkpoint inhibitors

null

《医学前沿（英文）》 2018年 第12卷 第2期   页码 229-235 doi: 10.1007/s11684-017-0581-0

摘要：

On May 23, 2017, the US Food and Drug Administration (FDA) approved a treatment for cancer patients with positive microsatellite instability-high (MSI-H) markers or mismatch repair deficient (dMMR) markers. This approach is the first approved tumor treatment using a common biomarker rather than specified tumor locations in the body. FDA previously approved Keytruda for treatment of several types of malignancies, such as metastatic melanoma, metastatic non-small-cell lung cancer, recurrent or metastatic head and neck cancer, refractory Hodgkin lymphoma, and urothelial carcinoma, all of which carry positive programmed death-1/programmed death-ligand 1 biomarkers. Therefore, indications of Keytruda significantly expanded. Several types of malignancies are disclosed by MSI-H status due to dMMR and characterized by increased neoantigen load, which elicits intense host immune response in tumor microenvironment, including portions of colorectal and gastric carcinomas. Currently, biomarker-based patient selection remains a challenge. Pathologists play important roles in evaluating histology and biomarker results and establishing detection methods. Taking gastric cancer as an example, its molecular classification is built on genome abnormalities, but it lacks acceptable clinical characteristics. Pathologists are expected to act as “genetic interpreters” or “genetic translators” and build a link between molecular subtypes with tumor histological features. Subsequently, by using their findings, oncologists will carry out targeted therapy based on molecular classification.

关键词： molecular classification     precision medicine     pembrolizumab     PD-1/PD-L1     MSI-H    

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A new and best approach for early detection of rotor and stator faults in induction motors coupled to

Abderrahim ALLAL,Boukhemis CHETATE

《能源前沿（英文）》 2016年 第10卷 第2期   页码 176-191 doi: 10.1007/s11708-015-0386-2

摘要： Today, induction machines are playing, thanks to their robustness, an important role in world industries. Although they are quite reliable, they have become the target of various types of defects. Thus, for a long time, many research laboratories have been focusing their works on the theme of diagnosis in order to find the most efficient technique to predict a fault in an early stage and to avoid an unplanned stopping in the chain of production and costs ensuing. In this paper, an approach called Park’s vector product approach (PVPA) was proposed which was endowed with a dominant sensitivity in the case in which there would be rotor or stator faults. To show its high sensitivity, it was compared with the classical methods such as motor current signature analysis (MCSA) and techniques studied in recent publications such as motor square current signature analysis (MSCSA), Park’s vector square modulus (PVSM) and Park-Hilbert (P-H) (PVSM ). The proposed technique was based on three main steps. First, the three-phase currents of the induction motor led to a Park’s vector. Secondly, the proposed PVPA was calculated to show the distinguishing spectral signatures of each default and specific frequencies. Finally, simulation and experimental results were presented to confirm the theoretical assumptions.

关键词： induction motor     incipient broken bar     extended Park’s vector approach     spectral analysis     inter-turn short-circuit     Hilbert transform    

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Identification of faults through wavelet transform vis-à-vis fast Fourier transform of noisy vibration

null

《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

摘要：

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    

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EAI-oriented information classification code system in manufacturing enterprises

WANG Junbiao, DENG Hu, JIANG Jianjun, YANG Binghong, WANG Bailing

《机械工程前沿（英文）》 2008年 第3卷 第1期   页码 81-85 doi: 10.1007/s11465-008-0011-8

摘要： Although the traditional information classification coding system in manufacturing enterprises (MEs) emphasizes the construction of code standards, it lacks the management of the code creation, code data transmission and so on. According to the demands of enterprise application integration (EAI) in manufacturing enterprises, an enterprise application integration oriented information classification code system (EAIO-ICCS) is proposed. EAIO-ICCS expands the connotation of the information classification code system and assures the identity of the codes in manufacturing enterprises with unified management of codes at the view of its lifecycle.

关键词： EAI     EAIO-ICCS     management     classification     connotation    

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用于手机屏缺陷检测的基于图的两阶段分类网络 Research Article

周超凡1,2,刘妹琴3,2,1,张森林1,2,魏平3,陈霸东3

《信息与电子工程前沿（英文）》 2023年 第24卷 第2期   页码 203-216 doi: 10.1631/FITEE.2200524

摘要： 缺陷检测是手机屏质量控制的重要环节。手机屏缺陷的特性带来了一些具有挑战性的问题，包括：（1）类间相似性和类内差异性；（2）低对比度、微小尺寸或不完整缺陷的识别带来的困难；（3）针对多标签图像的类别相关性建模。为了解决这些问题，本文提出一种图推理模块，它可以堆放在常规的分类模块上。该推理模块利用类别间的依赖性、图像间的关系以及类别图像之间的相互作用来扩展特征维度，并且达到改进低质量图像特征的目的。为了进一步提高分类性能，分类模块的分类器被设计为一个余弦相似度函数。在对比学习的帮助下，分类模块可以更好地初始化推理模块的类别图。在手机屏缺陷数据集上的实验表明，所提出的两阶段网络取得了最佳性能：准确率为97.7％，F-measure为97.3％。这证明了本文所提出的方法在工业应用中是有效的。

关键词： 基于图的方法；多标签分类；手机屏缺陷；神经网络    

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Fault classification and reconfiguration of distribution systems using equivalent capacity margin method

K. Sathish KUMAR, T. JAYABARATHI

《能源前沿（英文）》 2012年 第6卷 第4期   页码 394-402 doi: 10.1007/s11708-012-0211-0

摘要： This paper investigates the capability of support vector machines (SVM) for prediction of fault classification and the use of the concept of equivalent capacity margin (ECM) for restoration of the power system. The SVM, as a novel type of machine learning based on statistical learning theory, achieves good generalization ability by adopting a structural risk minimization (SRM) induction principle aimed at minimizing a bound on the generalization error of a model rather than the minimization of the error on the training data only. Here, the SVM has been used as a classification. The inputs of the SVM model are power and voltage values. An equation has been developed for the prediction of the fault in the power system based on the developed SVM model. The next steps of this paper are the restoration and reconfiguration by using the ECM concept, the development of a code, and the testing of the results with various load outages, which have been executed for a 12 load system.

关键词： support vector machines (SVM)     structural risk minimization (SRM)     equivalent capacity margin (ECM)     restoration     fault classification    

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标题 作者 时间 类型 操作

An approach for mechanical fault classification based on generalized discriminant analysis

LI Wei-hua, SHI Tie-lin, YANG Shu-zi

期刊论文

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

期刊论文

Urban landscape classification using Chinese advanced high-resolution satellite imagery and an object-orientedmulti-variable model

Li-gang MA,Jin-song DENG,Huai YANG,Yang HONG,Ke WANG

期刊论文

A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumerelectrical appliances classification

Chuan Choong YANG, Chit Siang SOH, Vooi Voon YAP

期刊论文

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

期刊论文

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

期刊论文

Condition monitoring of a wind turbine generator using a standalone wind turbine emulator

Himani,Ratna DAHIYA

期刊论文

Development of a new method for RMR and Q classification method to optimize support system in tunneling

Asghar RAHMATI,Lohrasb FARAMARZI,Manouchehr SANEI

期刊论文

知识推送系统中一种基于多分类径向基神经网络的知识匹配方法

张树有,顾叶,伊国栋,王自立

期刊论文

Molecular classification and precision therapy of cancer: immune checkpoint inhibitors

null

期刊论文

A new and best approach for early detection of rotor and stator faults in induction motors coupled to

Abderrahim ALLAL,Boukhemis CHETATE

期刊论文

Identification of faults through wavelet transform vis-à-vis fast Fourier transform of noisy vibration

null

期刊论文

EAI-oriented information classification code system in manufacturing enterprises

WANG Junbiao, DENG Hu, JIANG Jianjun, YANG Binghong, WANG Bailing

期刊论文

用于手机屏缺陷检测的基于图的两阶段分类网络

周超凡1,2,刘妹琴3,2,1,张森林1,2,魏平3,陈霸东3

期刊论文

Fault classification and reconfiguration of distribution systems using equivalent capacity margin method

K. Sathish KUMAR, T. JAYABARATHI

期刊论文