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Fault diagnosis of monoblock centrifugal pump using svm

ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2014年第3期17卷 152-157页

作者： Muralidharan, V. Sugumaran, V. Indira, V. BS Abdur Rahman Univ Dept Mech Engn Madras Tamil Nadu India VIT Univ Sch Mech & Bldg Sci Madras Tamil Nadu India PKAC Dept Math Kalitheerthalkuppam Puducherry India

Monoblock centrifugal pumps are employed in variety of critical engineering applications. Continuous monitoring of such machine component becomes essential in order to reduce the unnecessary break downs. At the outset, vibration based approaches are widely used to carry out the condition monitoring tasks. Particularly fuzzy logic, support vector machine (svm) and artificial neural networks were employed for continuous monitoring and fault diagnosis. In the present study, the application of svm algorithm in the field of fault diagnosis and condition monitoring is discussed. The continuous wavelet transforms were calculated for different families and at different levels. The computed transformation coefficients form the feature set for the classification of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different continuous wavelet families at different levels were calculated and compared to find the best wavelet for the fault diagnosis of the monoblock centrifugal pump. Copyright (C) 2014, Karabuk University. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Mono-block centrifugal pump svm algorithm Fault diagnosis Continuous wavelet transforms (CWT)

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A Model Divides the Mobile Security Level Based on svm

A Model Divides the Mobile Security Level Based on SVM

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2017 IEEE Conference on Communications and Network Security (CNS)

作者： Yang, Qing Jiang, Yixin Xu, Aidong Wen, Hong Wang, Feng Chen, LiuFei Ouyang, Kai Zhu, Xiping Univ Elec Sci & Tech China Natl Key Lab Commun Chengdu 610054 Peoples R China China Southern Power Grid Co Ltd EPRI Guangzhou Guangdong Peoples R China Chengdu Univ Informat Technol Coll Informat Secur Chengdu 610225 Sichuan Peoples R China

ISBN: (纸本)9781538606834

With the progress of the network and technology, the perfect combination of mobile intelligent terminal and internet, people are increasingly dependent on intelligent terminals. So, it was very necessary of a model for assessing the security performance of mobile intelligent terminals, especially to establish the objective model of the security performance of mobile intelligent terminal. In this paper, we propose a model that classification intelligent terminal security risk based on svm algorithm, and it can achieve the classification of mobile intelligent terminal security level objectivity and accurately. Such that users have an intuitive understanding for the intelligent mobile terminal security, by which the security needs can be met.

关键词： intelligent terminal risk assessment svm algorithm intelligent terminal threat

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Research on the UBI Car Insurance Rate Determination Model Based on the CNN-HVsvm algorithm

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IEEE ACCESS 2020年 8卷 160762-160773页

作者： Yan, Chun Wang, Xindong Liu, Xinhong Liu, Wei Liu, Jiahui Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China Beijing Inst Petrochem Technolog Dept Math & Phys Beijing 102617 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

With the support of Internet of Vehicles technology, UBI (Usage Based Insurance) car insurance rate determination has certain guiding significance for achieving accurate pricing of car insurance rates and satisfying the personalized needs of users. Based on the CNN (Convolutional Neural Networks) algorithm and svm (Support Vector Machine) algorithm, this paper establishes a rating model for UBI car insurance rates. The model first performs a series of operations such as convolutions, pooling and nonlinear activation function mapping using the CNN algorithm so that it can extract the features from the driving behavior data of UBI customers. Then, it introduces the Hull Vector to optimize the operating efficiency of the svm algorithm. The HVsvm (Hull Vector Support Vector Machine) algorithm classifies customers according to their driving behavior, and thus obtains UBI customer car insurance rate grades. Therefore, this paper proposes a UBI car insurance rate grade determination model based on the CNN-HVsvm algorithm. The empirical results of the model show that the CNN-HVsvm algorithm has higher discrimination accuracy in the risk rating process of UBI customer driving behavior than the CNN algorithm, BP neural network algorithm and svm algorithm;and when dealing with large training sets, it has a speed advantage over the CNN-svm algorithm. Furthermore, it is easy to realize in the process of establishing the UBI car insurance rate determination model and it has good robustness, which can adapt to diverse data sets, thus achieving better results in the car insurance rate determination process. Therefore, the CNN-HVsvm model can predict the grade of UBI car insurance users more accurately and efficiently, and the prediction results are more consistent with the actual situation, which has strong applicability and flexibility. The UBI car insurance premium rate determination model based on the CNN-HVsvm algorithm can determine driver behavior more fairly and reasonably,

关键词： Insurance Automobiles Support vector machines Industries Prediction algorithms Big Data CNN algorithm CNN-HVsvm algorithm rate grade judgment svm algorithm UBI car insurance

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Fault Diagnosis of Aviation Secondary Power Supply Based on svm 14

Fault Diagnosis of Aviation Secondary Power Supply Based on ...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Che, Shuangxi Zhang, Yanxi Chen, Runzhe Ma, Yaoxuan Li, Xing Wang, Li College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9798350301359

Timely and effective fault diagnosis for aviation secondary power supplies to minimize the failure rate and ensure the power supply of the load is critical. In this paper, in order to realize the fault diagnosis of aviation secondary power supply main topology phase shifted full bridge (PSFB) converter, the working principle, fault mechanism, mode, modelling method are studied. Based on the fault data analysis and feature extraction, the fault diagnosis is carried out by using support vector machine (svm) algorithm, the results of which show that the methodology proposed in this paper has a high accuracy rate. © 2023 IEEE.

关键词： Falut diagnosis PSFB converter svm algorithm

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Estimation of leaf color variances of Cotinus coggygria based on geographic and environmental variables

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Journal of Forestry Research 2021年第2期32卷 609-622页

作者： Xing Tan Jiaojiao Wu Yun Liu Shixia Huang Lan Gao Wen Zhang College of Resources and Environment Southwest UniversityChongqing 400716People’s Republic of China

Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of *** this study,leaf color variances of the Eurasian smoke tree,Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net(GELnet)and support vector machine(svm)*** reveal that leaf color varied over space,and the variances were the result of geography due to its effect on solar radiation,temperature,illumination and moisture of the shrub environment,whereas the influence of climate were not *** svm and GELnet algorithm models were similar estimating leaf color indices based on geographic variables,and demonstrates that both techniques have the potential to estimate leaf color variances of *** in a shrubbery with a complex geographical environment in the absence of human activity.

关键词： RGB color space Digital elevation model Variable selection svm algorithm GELnet algorithm

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Design and verification of use case generation algorithm based on multiple combination tests

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2024年第2期23卷 69-79页

作者： Zheng, Genrang Sch Informat Engn Zhongshan Polytech Zhongshan 528404 Peoples R China

To solve the problem of low accuracy of interface test cases, an AdaBoost + svm use case generation algorithm based on multiple combination tests is proposed. Multiple svm classifiers are homogeneously integrated by using serial AdaBoost integration methods, and they are simulated by using the PICT combination test tools. The results show that the proposed AdaBoost + svm use case generation algorithm can generate multiple combination interface test cases automatically, and it has a strong interface defect detection capability. Compared with the use case generation algorithm of single svm classifier and parallel bagging integrated svm classifier and the current popular use case generation algorithm, the proposed AdaBoost + svm algorithm performs better in accuracy, precision, recall, F1 value and AUC values, which has certain advantages and can improve the accuracy of interface test.

关键词： combination test interface test use case generation svm algorithm ensemble learning

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Near-surface snowmelt detection on the Greenland ice sheet from FengYun-3 MWRI data

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8301-S8308页

作者： Wang, Xingdong Wu, Zhankai Li, Xinwu Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Henan Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China Fujian Normal Univ Key Lab OptoElect Sci & Technol Med Fujian Prov Key Lab Photon Technol Minist Educ Fuzhou Fujian Peoples R China

The melt extent on the Greenland ice sheet plays an important role in energy balance, and the Arctic and global climates. The micro-wave radiation imager (MWRI) is one of the major payloads of Chinese second-generation polar-orbiting meteorological satellite, FengYun-3 (FY-3), and it is similar to the special sensor microwave/image (SSM/I). The cross-polarized gradient ratio (XPGR) is mainly applied in the scanning multichannel microwave radiometer (SMMR) (18 GHz horizontal polarization (18 H) and 37 GHz vertical polarization (37 V)), the advanced microwave scanning radiometer-earth observing system (AMSR-E) (18.7 GHz horizontal polarization (18 H) and 36.5 GHz vertical polarization (36 V)) and SSM/I (19.3 GHz horizontal polarization (19 H) and 37 GHz vertical polarization (37 V)), which increases the differences between dry and wet snow. The hyperplane of support vector machine (svm) is used to detect the melt information based on the XPGR data on the Greenland ice sheet, which has higher detection accuracy comparing with the existing threshold methods in theory. The results were compared with the SSM/I data (threshold = - 0.0154), and the results show that the proposed method (That is XPGR combining with svm) for MWRI data is feasible for the detection of the near-surface snowmelt information on the Greenland ice sheet.

关键词： FengYun-3 satellite Snowmelt detection Greenland ice sheet XPGR algorithm svm algorithm

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Auto-classification of biomass through characterization of their pyrolysis behaviors using thermogravimetric analysis with support vector machine algorithm: case study for tobacco

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BIOTECHNOLOGY FOR BIOFUELS 2021年第1期14卷 1页

作者： Yin, Chao Deng, Xiaohua Yu, Zhiqiang Liu, Zechun Zhong, Hongxiang Chen, Ruting Cai, Guohua Zheng, Quanxing Liu, Xiucai Zhong, Jiawei Ma, Pengfei He, Wei Lin, Kai Li, Qiaoling Wu, Anan Xiamen Univ Coll Chem & Chem Engn Fujian Prov Key Lab Theoret & Computat Chem Xiamen 361005 Fujian Peoples R China China Tobacco Fujian Ind Co Ltd Technol Ctr Xiamen 361021 Fujian Peoples R China

Background During the biomass-to-bio-oil conversion process, many studies focus on studying the association between biomass and bio-products using near-infrared spectra (NIR) and chemical analysis methods. However, the characterization of biomass pyrolysis behaviors using thermogravimetric analysis (TGA) with support vector machine (svm) algorithm has not been reported. In this study, tobacco was chosen as the object for biomass, because the cigarette smoke (including water, tar, and gases) released by tobacco pyrolysis reactions decides the sensory quality, which is similar to biomass as a renewable resource through the pyrolysis process. Results svm algorithm has been employed to automatically classify the planting area and growing position of tobacco leaves using thermogravimetric analysis data as the information source for the first time. Eighty-eight single-grade tobacco samples belonging to four grades and eight categories were split into the training, validation, and blind testing sets. Our model showed excellent performances in both the training and validation set as well as in the blind test, with accuracy over 91.67%. Throughout the whole dataset of 88 samples, our model not only provides precise results on the planting area of tobacco leave, but also accurately distinguishes the major grades among the upper, lower, and middle positions. The error only occurs in the classification of subgrades of the middle position. Conclusions From the case study of tobacco, our results validated the feasibility of using TGA with svm algorithm as an objective and fast method for auto-classification of tobacco planting area and growing position. In view of the high similarity between tobacco and other biomasses in the compositions and pyrolysis behaviors, this new protocol, which couples the TGA data with svm algorithm, can potentially be extrapolated to the auto-classification of other biomass types.

关键词： Thermogravimetric analysis Machine learning svm algorithm Tobacco

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Corrosion inhibition by Myristica fragrans extract in neem biodiesel: Evaluation by machine learning and traditional methods

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第17期238卷 8829-8837页

作者： Iyyappan, S. Kumar, K. P. Vinod Ponram, P. Ponjesly Coll Engn Dept Mech Engn Nagercoil Tamil Nadu India Univ Coll Engn Dept Chem Nagercoil 629004 Tamil Nadu India Univ Coll Engn Dept Elect & Commun Engn Nagercoil Tamil Nadu India

Ethyl acetate extract of Myristica fragrans mace flowers was investigated as an effective inhibitor for copper in neem biodiesel. Employing the ASTM D130 method to assess copper corrosion in biodiesel, a remarkable inhibition efficiency of 98.04% was achieved. Inhibitor adsorption on the surface of copper metal was substantiated by FTIR spectral evaluation. Surface morphology studies by SEM confirmed the protective nature of the inhibitor for copper metal corrosion prevention. Further, several support vector machine (svm) learning algorithms were employed with three different classes of corrosion to study the corrosion inhibition of which, the medium Gaussian procedure gave a maximum accuracy of 92.4%. The work reveals the applicability of the svm algorithm for accurate corrosion estimation and classification not only in biodiesel medium but also in various environments.

关键词： svm algorithm corrosion inhibitors machine learning biodiesel corrosion

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Design of Smart Home Implementation Within IoT Natural Language Interface

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IEEE ACCESS 2020年 8卷 84929-84949页

作者： Kim, Tae-Yeun Bae, Sang-Hyun An, Young-Eun Chosun Univ SW Convergence Educ Inst Gwangju 61452 South Korea Chosun Univ Dept Comp Sci & Stat Gwangju 61452 South Korea Chosun Univ Coll Gen Educ Gwangju 61452 South Korea

To process continuous sensor data in Internet of Things (IoT) environments, this study optimizes queries using multiple MJoin operators. To achieve efficient storage management, it classifies and reduces data using a support vector machine (svm) classification algorithm. A global shared query execution technique was used to optimize multiple MJoin queries. By comparing each kernel function of the svm classification algorithm, the system's performance was evaluated through experiments according to the selected optimal kernel function and changes in sliding window size. Furthermore, to implement a smart home system that can actively respond to users, classified and reduced sensor data were utilized to enable the intelligent control of devices inside the home. The sensor data (e.g., temperature, humidity, gas) used to recognize the current conditions of an IoT-based smart home system and corresponding date data were classified into decision trees, and the system was designed using five sensors to intelligently control priorities such as ventilation, temperature, and fire and intrusion detection. The experiments demonstrated that the multiple MJoin technique yields high improvements in performance with relatively few searches. In this study, the sigmoid was selected as the optimal kernel function for the svm classification algorithm. According to the svm classification algorithm results, based on changes in the sliding window size, the average error rate was 2.42%, the reduction result was 17.58%, and the classification accuracy was 85.94%. According to the comparison of the classification performance of svm and other algorithms, the svm classification algorithm exhibited a minimum 9% better classification performance. Thus, compared to existing home systems, this algorithm is expected to increase system efficiency and convenience by enabling the configuration of a more intelligent environment according to the user's characteristics or requirements.

关键词： Application Internet of Things (IoT) sensor data smart home system svm algorithm

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