Infinite AdaBoost and its Application on Fault Diagnosis for Analog Circuits

2011 ◽  
Vol 201-203 ◽  
pp. 2070-2074
Author(s):  
He Xun Wang ◽  
Chong Liu ◽  
Yu Qing Sun
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Ensemble Learning ◽  
Analog Circuits ◽  
Classification Accuracy ◽  
Experimental Results ◽  
Support Vector ◽  
Adaboost Algorithm ◽  
Learning Framework

AdaBoost algorithm can achieve better performance by averaging over the predictions of some weak hypotheses. To improve the power of classification ability of AdaBoost, an infinite ensemble learning framework based on the Support Vector Machine was formulated. The framework can output an infinite AdaBoost through embedding infinite hypotheses into a new kernel of Support Vector Machine. The stump kernel embodies infinite decision stumps. At last, the algorithm was used in fault diagnosis for analog circuits. Experimental results show that infinite AdaBoost with Support Vector Machine is superior than finite AdaBoost with the same base hypothesis set. The purpose of enhancing classification accuracy of AdaBoost algorithm is achieved.

Analog Circuits Fault Diagnosis Using Multifractal Analysis

2013 ◽  
Vol 721 ◽  
pp. 367-371
Author(s):  
Yong Kui Sun ◽  
Zhi Bin Yu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Analog Circuits ◽  
Multifractal Analysis ◽  
Multifractal Spectrum ◽  
Experimental Results ◽  
Support Vector ◽  
Diagnosis Accuracy ◽  
Circuit Under Test

Analog circuits fault diagnosis using multifractal analysis is presented in this paper. The faulty response of circuit under test is analyzed by multifratal formalism, and the fault feature consists of multifractal spectrum parameters. Support vector machine is used to identify the faults. Experimental results prove the proposed method is effective and the diagnosis accuracy reaches 98%.

scholarly journals Fault Diagnosis of Rolling Bearings in Rail Train Based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm

2019 ◽  
Vol 9 (15) ◽  
pp. 3143 ◽  
Author(s):  
Lu Han ◽  
Chongchong Yu ◽  
Cuiling Liu ◽  
Yong Qin ◽  
Shijie Cui
Keyword(s):  
Fault Diagnosis ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Rolling Bearing ◽  
Exponential Smoothing ◽  
Working Environment ◽  
Support Vector ◽  
Adaboost Algorithm ◽  
Vibration Data ◽  
Ensemble Learning Algorithm

The rolling bearing is a key component of the bogie of the rail train. The working environment is complex, and it is easy to cause cracks and other faults. Effective rolling bearing fault diagnosis can provide an important guarantee for the safe operation of the track while improving the resource utilization of the rolling bearing and greatly reducing the cost of operation. Aiming at the problem that the characteristics of the vibration data of the rolling bearing components of the railway train and the vibration mechanism model are difficult to establish, a method for long-term faults diagnosis of the rolling bearing of rail trains based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm is proposed. Firstly, the sliding time window segmentation algorithm of exponential smoothing is used to segment the rolling bearing vibration data, and then the segmentation points are used to construct the localized features of the data. Finally, an Improved AdaBoost Algorithm (IAA) is proposed to enhance the anti-noise ability. IAA, Back Propagation (BP) neural network, Support Vector Machine (SVM), and AdaBoost are used to classify the same dataset, and the evaluation indexes show that the IAA has the best classification effect. The article selects the raw data of the bearing experiment platform provided by the State Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University and the standard dataset of the American Case Western Reserve University for the experiment. Theoretical analysis and experimental results show the effectiveness and practicability of the proposed method.

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 203-207
Author(s):  
Jian Hua Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Electrical Control ◽  
Diagnosis Method ◽  
The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

Application of ACO-SVM in Chinese Text Feature Selection

2012 ◽  
Vol 155-156 ◽  
pp. 770-775
Author(s):  
Jin Xiu Cui ◽  
Xiao Xia Huang
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Chinese Text ◽  
Classification Accuracy ◽  
Considerable Increase ◽  
Experimental Results ◽  
Support Vector ◽  
Text Feature

The algorithm proposed in this paper applies ACO in combination with support vector machine (SVM) in Chinese text feature selection. It obtains classifier models for each category at last. The experimental results show that the proposed method is feasible and lead to a considerable increase of classification accuracy.

scholarly journals Personal Identification Based on Vectorcardiogram Derived from Limb Leads Electrocardiogram

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Jongshill Lee ◽  
Youngjoon Chee ◽  
Inyoung Kim
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Personal Identification ◽  
Experimental Results ◽  
New Method ◽  
Support Vector ◽  
Inverse Transform ◽  
Transform Matrix ◽  
Electrocardiogram Ecg

We propose a new method for personal identification using the derived vectorcardiogram (dVCG), which is derived from the limb leads electrocardiogram (ECG). The dVCG was calculated from the standard limb leads ECG using the precalculated inverse transform matrix. Twenty-one features were extracted from the dVCG, and some or all of these 21 features were used in support vector machine (SVM) learning and in tests. The classification accuracy was 99.53%, which is similar to the previous dVCG analysis using the standard 12-lead ECG. Our experimental results show that it is possible to identify a person by features extracted from a dVCG derived from limb leads only. Hence, only three electrodes have to be attached to the person to be identified, which can reduce the effort required to connect electrodes and calculate the dVCG.

scholarly journals Analysis and Study of Diabetes Follow-Up Data Using a Data-Mining-Based Approach in New Urban Area of Urumqi, Xinjiang, China, 2016-2017

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yukai Li ◽  
Huling Li ◽  
Hua Yao
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Decision Tree ◽  
Urban Area ◽  
Ensemble Learning ◽  
Learning Model ◽  
Support Vector ◽  
Test Set ◽  
Adaboost Algorithm

The focus of this study is the use of machine learning methods that combine feature selection and imbalanced process (SMOTE algorithm) to classify and predict diabetes follow-up control satisfaction data. After the feature selection and unbalanced process, diabetes follow-up data of the New Urban Area of Urumqi, Xinjiang, was used as input variables of support vector machine (SVM), decision tree, and integrated learning model (Adaboost and Bagging) for modeling and prediction. The experimental results show that Adaboost algorithm produces better classification results. For the test set, the G-mean was 94.65%, the area under the ROC curve (AUC) was 0.9817, and the important variables in the classification process, fasting blood glucose, age, and BMI were given. The performance of the decision tree model in the test set is relatively lower than that of the support vector machine and the ensemble learning model. The prediction results of these classification models are sufficient. Compared with a single classifier, ensemble learning algorithms show different degrees of increase in classification accuracy. The Adaboost algorithm can be used for the prediction of diabetes follow-up and control satisfaction data.

Classification of P2P Traffic Based on a Heteromorphic Ensemble Learning Model

2014 ◽  
Vol 687-691 ◽  
pp. 2693-2697
Author(s):  
Li Ding ◽  
Li Mao ◽  
Xiao Feng Wang
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Ensemble Learning ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Learning Model ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Environment

One single machine learning algorithm presents shortcomings when the data environment changes in the process of application. This article puts forward a heteromorphic ensemble learning model made up of bayes, support vector machine (SVM) and decision tree which classifies P2P traffic by voting principle. The experiment shows that the model can significantly improve the classification accuracy, and has a good stability.

scholarly journals Multi-Strategy Ensemble Whale Optimization Algorithm and Its Application to Analog Circuits Intelligent Fault Diagnosis

2020 ◽  
Vol 10 (11) ◽  
pp. 3667 ◽  
Author(s):  
Xianfeng Yuan ◽  
Zhaoming Miao ◽  
Ziao Liu ◽  
Zichen Yan ◽  
Fengyu Zhou
Keyword(s):  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Analog Circuits ◽  
Experimental Results ◽  
Whale Optimization Algorithm ◽  
Support Vector ◽  
Initial Population ◽  
Whale Optimization ◽  
The One ◽  
Searching Ability

The whale optimization algorithm (WOA) is a new swarm intelligence (SI) optimization algorithm, which has the superiorities of fewer parameters and stronger searching ability. However, previous studies have indicated that there are shortages in maintaining diversity and avoiding local optimal solutions. This paper proposes a multi-strategy ensemble whale optimization algorithm (MSWOA) to alleviate these deficiencies. First, the chaotic initialization strategy is performed to enhance the quality of the initial population. Then, an improved random searching mechanism is designed to reduce blindness in the exploration phase and speed up the convergence. In addition, the original spiral updating position is modified by the Levy flight strategy, which leads to a better tradeoff between local and global search. Finally, an enhanced position revising mechanism is utilized to improve the exploration further. To testify the superiorities of the proposed MSWOA algorithm, a series of comparative experiments are carried out. On the one hand, the numerical optimization experimental results, which are conducted under nineteen widely used benchmark functions, indicate that the performance of MSWOA stands out compared with the standard WOA and six other well-designed SI algorithms. On the other hand, MSWOA is utilized to tune the parameters of the support vector machine (SVM), which is applied to the fault diagnosis of analog circuits. Experimental results confirm that the proposed method has higher diagnosis accuracy than other competitors. Therefore, the MSWOA is successfully applied as a novel and efficient optimization algorithm.

Experimental Frequency-Domain Vibration Based Fault Diagnosis of Roller Element Bearings Using Support Vector Machine

2020 ◽  
Author(s):  
Vishal G Salunkhe ◽  
Ramchandra Ganapati Desavale ◽  
Jagadeesha T.
Keyword(s):  
Mathematical Model ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Experimental Results ◽  
Life Extension ◽  
Support Vector ◽  
Bearing Failure ◽  
Rotating Machines ◽  
Experimental Frequency ◽  
Model Based

Abstract In heavy rotating machines and assembly lines, bearing failure in any one of the rotating machines results in shut down of many other machines and affects the overall cost and quality of the product. Condition monitoring of bearing systems avoids breakdown and saves preventive and corrective maintenance time and cost. This research paper proposes advanced strategies in early fault detection of taper rolling bearings. In view of this, a mathematical model based- fault diagnosis and support vector machine (SVM) is proposed in this work. The mathematical model using dimension analysis by matrix method (DAMM) and SVM is developed to predict the vibration characteristic of the rotor bearing system. Various types of defects created using an electric discharge machine (EDM) are analyzed by correlating dependent and independent parameters. Experiments were performed to classify the rotor dynamic characteristic of healthy and unhealthy bearing. Experimental results are used to validate the model obtained by DAMM and SVM. Experimental results showed that vibration characteristics are evaluated by using a theoretical model and SVM. This contribution to the service life extension and efficiency improvement, so as to reduce bearing failure. Thus, the automatic online diagnosis of bearing faults is possible with a developed model-based by DAMM and SVM.

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