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Vibration based fault diagnosis of monoblock centrifugal pump using decision tree

EXPERT SYSTEMS WITH APPLIcATIONS 2010年第6期37卷 4040-4049页

作者： Sakthivel, N. R. Sugumaran, V. Babudevasenapati, S. SRM Univ Dept Mech Engn Madras Tamil Nadu India Amrita Sch Engn Dept Mech Engn Coimbatore 641105 Tamil Nadu India

Monoblock centrifugal pumps are widely used in a variety of applications. In many applications the role of monoblock centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly artificial neural networks, fuzzy logic were employed for continuous monitoring and fault diagnosis. This paper presents the use of c4.5 decision tree algorithm for fault diagnosis through statistical feature extracted from vibration signals of good and faulty conditions. (c) 2009 Elsevier Ltd. All rights reserved.

关键词： Monoblock centrifugal pump c4.5 algorithm Fault diagnosis Statistical features

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A Fast channel change Technique Based on channel Prediction

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IEEE TRANSAcTIONS ON cONSUMER ELEcTRONIcS 2018年第4期64卷 418-423页

作者： Basicevic, Ilija Kukolj, Dragan Ocovaj, Stanislav cmiljanovic, Gordana Fimic, Nemanja Univ Novi Sad Fac Tech Sci Novi Sad 21000 Serbia RTRK Res Inst IPR Dept Novi Sad 21000 Serbia RTRK Res Inst Embedded Syst Dept Novi Sad 21000 Serbia

The channel zapping delay problem is a well-known problem in digital television. It has been researched quite extensively in the context of IPTV. In this paper, we present a technique for delay reduction that predicts the next channel that will be selected by the user. The technique is applicable to DVB-c/S/T, as well as in IPTV. The prediction is based on an adaptive model, which is built during a training process in which the history of channel changes is collected. The model is afterwards constantly updated. The problem is approached as a classification problem and c4.5 decision trees have shown the best performance among the tested algorithms. The proposed technique has been evaluated using channel history datasets obtained by simulation. The results of the evaluation show an increase of 5.67% in the probability that the next selected channel is already pre-joined, compared to the case when only neighboring channels are pre-joined. The application of the proposed technique is explained in detail.

关键词： c4.5 algorithm digital television fast channel change machine learning zapping delay

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An experimental study on symbolic extreme learning machine

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INTERNATIONAL JOURNAL OF MAcHINE LEARNING AND cYBERNETIcS 2019年第4期10卷 787-797页

作者： Liu, Jinga Patwary, Muhammed J. A. Sun, XiaoYun Tao, Kai Shijiazhuang Tiedao Univ Coll Elect & Elect Engn Shijiazhuang Hebei Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Big Data Inst Shenzhen Guangdong Peoples R China

With the advent of big data era, the volume and complexity of data have increased exponentially and the type of data has also been increased largely. Among all different types of data, symbolic data plays an important role in the study on machine learning model. It has been proved that feed-forward neural network (FNN) has a good ability to deal with numeric data but relatively clumsy with symbolic data. In this paper, a special type of FNN called Extreme Learning Machine (ELM) is discussed for handling symbolic data. Experimental results demonstrate that, unlike traditional back propagation based FNN, ELM has a better performance in comparison with c4.5 which is generally acknowledged as one of the best algorithms in handling symbolic data classification problems. In this performance comparison, some key evaluation criteria such as generalization ability, time complexity, the effect of training sample size and noise-resistance ability are taken into account.

关键词： Big data FNN Symbolic ELM c4.5 algorithm

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Improving naive Bayes classifier by dividing its decision regions

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Journal of Zhejiang University-Science c(computers and Electronics) 2011年第8期12卷 647-657页

作者： Zhi-yong YAN Gong-fu XU Yun-he PAN Institute of Artificial Intelligence Zhejiang University Hangzhou 310027 China

classification can be regarded as dividing the data space into decision regions separated by decision *** this paper we analyze decision tree algorithms and the NBTree algorithm from this ***,a decision tree can be regarded as a classifier tree,in which each classifier on a non-root node is trained in decision regions of the classifier on the parent ***,the NBTree algorithm,which generates a classifier tree with the c4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively,can also be regarded as training naive Bayes classifiers in decision regions of the c4.5 *** propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes *** four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root *** SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries,and those that may be ignored by the naive Bayes ***,we conduct experiments on 30 data sets from the Uc Irvine (UcI) *** results show that the SD algorithm can obtain better generali-zation abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the c4.5 algorithm and support vector machine (SVM) as leaf *** experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.

关键词： Naive Bayes classifier Decision region NBTree c4.5 algorithm Support vector machine (SVM)

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Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

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MEcHANIcAL SYSTEMS AND SIGNAL PROcESSING 2015年第1期52-53卷 436-446页

作者： Jegadeeshwaran, R. Sugumaran, V. VIT Univ Sch Mech & Bldg Sci Madras 600127 Tamil Nadu India

Hydraulic brakes in automobiles are important components for the safety of passengers;therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the c4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the c-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Decision tree Statistical features c4.5 algorithm SVM Kernel function RBF

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A management of early warning and risk control based on data fusion for cOVID-19

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第6期39卷 8989-8996页

作者： Yan, Hongru chai, Huaqi Dai, Yang Northwestern Polytech Univ Sch Management 1 Dongxiang Rd Xian Shaanxi Peoples R China Xian Polytech Univ Sch Mech & Elect Engn Xian Shaanxi Peoples R China

According to the previous management of early warning and risk control methods, the efficiency of management prediction is low, the effect is not good, and the disadvantages are very obvious. This paper mainly studies the c4.5 algorithm, Apriori algorithm and K-means algorithm. On the basis of association rules, the data from the above three algorithms are fused. On the fusion results of the processed data, it builds and optimizes the early warning model. The fusion data used in this model can be regarded as the basic data and the association rules are used for data mining The experimental results show that data fusion can solve the problems of management early warning and risk control. This method is applied to enterprises Management has reference value.

关键词： c4.5 algorithm association rules early warning model association analysis K-means algorithm

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cT4RDD: classification trees for research on digital divide

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EXPERT SYSTEMS WITH APPLIcATIONS 2013年第14期40卷 5779-5786页

作者： coria, Sergio R. Mondragon-Becerra, Rosibelda Perez-Meza, Monica Ramirez-Vasquez, Sandra K. Martinez-Pelaez, Rafael Barragan-Lopez, Dario Avila-Barron, Oswaldo R. Univ Sierra Sur Inst Informat Miahuatlan Porfirio Diaz 70800 Oaxaca Mexico

This paper presents cT4RDD (classification trees for research on digital divide), a novel methodology for the quantitative analysis and modeling of the digital divide phenomenon with an approach of single country. It is inspired on the reputed Quinlan's c4.5 algorithm to automatically produce classification trees, as implemented in Witten & Frank's WEKA software toolkit. The methodology is created and evaluated on data from the 2010 Mexican Population and Housing census that include a number of variables whose interactions involve aspects of the phenomenon;particularly, interactions among Internet service presence in households and a number of features regarding educational and economical levels, genders, ages, housing characteristics, ratios of indigenous population, etc. Discretization is used to represent percentages of presence of Internet in households of municipalities as a nominal target attribute to produce classification trees. Results suggest that the methodology can produce quantitative profiles that describe similarities and differences among a series of municipality classes that present different percentages of presence of Internet in households. The discovered profiles provide scholars, government officials and enterprise managers with valuable insight for research, planning and decision making. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Digital divide analysis Digital divide modeling Digital divide measurement census data mining c4.5 algorithm J4.8 algorithm

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Development of Depression Prediction Models for caregivers of Patients with Dementia Using Decision Tree Learning algorithm

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INTERNATIONAL JOURNAL OF GERONTOLOGY 2019年第4期13卷 314-319页

作者： Byeon, Haewon Honam Univ Dept Speech Language Pathol Coll Hlth Sci Gwangju South Korea

Background: The objective of this study was to develop a depression prediction model customized for South Korean dementia caregivers by combining the RBF artificial neural network and the c4.5 algorithm. Method: The study subjects were selected from 228,558 people who completed the community health investigation. A total of 2,421 caregivers (1109 males and 1312 females) aged 60 years or older who completed mental health questionnaires were analyzed. Depression, a result variable, was defined using the center for Epidemiological Studies Depression (cES-D: a Korean version). Results: This study constructed a statistical classification model using the c4.5 algorithm and the model showed that subjective health condition was the most important predictive factor associated with the depression of the dementia caregivers. conclusion: The results of this study suggest that it will be necessary to develop a customized program for predicting the depression of the caregivers, who take care of the demented elderly, efficiently and managing it. copyright (c) 2019, Taiwan Society of Geriatric Emergency & critical care Medicine.

关键词： c4.5 algorithm decision tree learning dementia caregiver depression

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Evaluation research on data processing of mental health of college students based on decision tree algorithm

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JOURNAL OF cOMPUTATIONAL METHODS IN ScIENcES AND ENGINEERING 2019年第4期19卷 1101-1108页

作者： Zhou, Lijie Guizhou Univ Finance & Econ Guiyang 550025 Guizhou Peoples R China

With the popularization of higher education in china, a series of social problems caused by mental health problems rise frequently because of the imperfect mental quality of college students, attracting attention of the school and society. To gain insight into the main mental symptoms and factors affecting their mental health, decision tree owns an incomparable advantage, which is an important means in data mining In this study, methods and procedures of decision tree construction were introduced and decision tree c4.5 algorithm was elaborated. In this algorithm, appropriate attributes were selected as tree roots and subtree roots, and then the decision tree was constructed through repetition. Decision tree algorithm could classify the mental health of college students precisely, analyze rules and guide mental health counselors with correct plans, which was helpful to the decision making and mental health of college students improving.

关键词： Mental health decision tree c4.5 algorithm

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climate patterns of political division units obtained using automatic classification trees

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ATMOSFERA 2016年第4期29卷 359-377页

作者： coria, Sergio R. Gay-Garcia, carlos Villers-Ruiz, Lourdes Guzman-Arenas, Adolfo Sanchez-Meneses, Oscar Avila-Barron, Oswaldo R. Perez-Meza, Monica cruz-Nunez, Xochitl Lorenzo Martinez-Luna, Gilberto Univ Nacl Autonoma Mexico Ctr Ciencias Atmosfera Circuito Invest Cient S-NCiudad Univ Ciudad De Mexico 04510 DF Mexico Univ Sierra Sur Inst Informat Guillermo Rojas Mijangos S-N Esq Av Univ Miahuatlan De Porfirio D 70800 Oaxaca Mexico Inst Politecn Nacl Ctr Invest Computac Av Juan de Dios Batiz Esq Miguel Othon de Mendiza Mexico City 07738 DF Mexico

This article proposes a methodology to discover patterns in observed climatologic data, particularly temperatures and rainfall, in subnational political division units using an automatic classification algorithm (a decision tree produced by the c4.5 algorithm). Thus, the patterns represent classification trees, assuming that: (1) every political division unit contains at least one climatological station, and (2) the recording periods of the stations are relatively similar in duration and in their initial and ending years. A series of classification models are produced by using different subsets from an experimental dataset. This dataset contains information from 3606 climatological stations in Mexico with recording periods whose durations, initial and ending years are diverse. The target (dependent) variable in all these models is the name of the political unit (i.e., the state). The predictors are 36 monthly features per each climatological station: 12 features corresponding to a minimum temperature, 12 to a maximum temperature, and 12 to cumulative rainfall. The altitude feature is also used as one of the predictors, in addition to the other 36;however, it is used only to quantify its additional contribution to the modelling. The results show that classification trees are effective models for describing and representing non-trivial patterns to characterize the political division units based on their monthly temperatures and rainfalls. One of the remarkable findings is that the cumulative rainfall of May is the feature with highest discrimination capability to the characterization task, which is consistent with the theoretical background on Mexican climatology. In addition, classification trees offer higher expressivity to non-experts in machine learning.

关键词： climate patterns political division Mexico climate data mining data science classification algorithms classification trees c4.5 algorithm

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