Classification of knee-joint vibroarthrographic signals using time-domain and time-frequency domain features and least-squares support vector machine

2009 ◽  
Author(s):  
Yunfeng Wu ◽  
Sridhar Krishnan
Keyword(s):  
Support Vector Machine ◽  
Knee Joint ◽  
Least Squares ◽  
Frequency Domain ◽  
Time Domain ◽  
Support Vector ◽  
Time Frequency ◽  
Vibroarthrographic Signals

scholarly journals Application of Xgboost Feature Extraction in Fault Diagnosis of Rolling Bearing

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Xingang WANG ◽  
Chao WANG
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Support Vector ◽  
Time Frequency ◽  
Diagnosis Accuracy

Due to the difficulty that excessive feature dimension in fault diagnosis of rolling bearing will lead to the decrease of classification accuracy, a fault diagnosis method based on Xgboost algorithm feature extraction is proposed. When the Xgboost algorithm classifies features, it generates an order of importance of the input features. The time domain features were extracted from the vibration signal of the rolling bearing, the time-frequency features were formed by the singular value of the modal components that were decomposed by the variational mode decomposition. Firstly, the extracted time domain and time-frequency domain features were input into the support vector machine respectively to observe the fault diagnosis accuracy. Then, Xgboost algorithm was used to rank the importance of features and got the accuracy of fault diagnosis. Finally, important features were extracted and the extracted features were input into the support vector machine to observe the fault diagnosis accuracy. The result shows that the fault diagnosis accuracy of rolling bearing is improved after important feature extraction in time domain and time-frequency domain by Xgboost.

Optimization of support vector machine based multi-fault classification with evolutionary algorithms from time domain vibration data of gears

2013 ◽  
Vol 227 (11) ◽  
pp. 2428-2439 ◽  
Author(s):  
DJ Bordoloi ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Time Domain ◽  
Angular Speed ◽  
Fault Classification ◽  
Support Vector ◽  
Vibration Data ◽  
Bee Colony ◽  
Time Domain Data ◽  
Grid Search Method

In the present work, a multi-fault classification of gears has been attempted by the support vector machine learning technique using the vibration data in time domain. A proper utilization of the support vector machine is based on the selection of support vector machine parameters. The main focus of this article is to examine the performance of the multiclass ability of support vector machine techniques by optimizing its parameters using the grid-search method, genetic algorithm and artificial bee colony algorithm. Four fault conditions were considered. A group of statistical features were extracted from time domain data. The prediction of fault classification is attempted at the same angular speed as the measured data as well as innovatively at the intermediate and extrapolated angular speed conditions. This is due to the fact that it is not feasible to have measurement of vibration data at all continuous speeds of interest. The classification ability is noted and it shows an excellent prediction performance.

scholarly journals sEMG based Classification of Hand Gestures using EMD Algorithm and its Application on Control System

2019 ◽  
Vol 8 (11S) ◽  
pp. 1103-1105
Keyword(s):  
Support Vector Machine ◽  
Control System ◽  
Empirical Mode Decomposition ◽  
Time Domain ◽  
Support Vector ◽  
Hand Gestures ◽  
Mode Decomposition ◽  
Single Sensor ◽  
Mean Variance

Single sensor is employed for classifying four hand gestures from flexor carpum ulnaris. The first three IMFs that are obtained as a result of Empirical Mode Decomposition are taken into consideration. Time domain features like mean, variance, skewness, etc are taken for each IMFs. Support Vector Machine was used for classification task and the extracted model is used for making predictions

The Classification of Skateboarding Tricks by Means of Support Vector Machine: An Evaluation of Significant Time-Domain Features

2020 ◽  
pp. 125-132
Author(s):  
Muhammad Amirul Abdullah ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Anwar P. P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Time Domain ◽  
Support Vector ◽  
Significant Time

A MODIFIED LEAST SQUARES SUPPORT VECTOR MACHINE CLASSIFIER WITH APPLICATION TO CREDIT RISK ANALYSIS

2009 ◽  
Vol 08 (04) ◽  
pp. 697-710 ◽  
Author(s):  
LEAN YU ◽  
SHOUYANG WANG ◽  
JIE CAO
Keyword(s):  
Support Vector Machine ◽  
Risk Analysis ◽  
Credit Risk ◽  
Least Squares ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Original Form ◽  
Simple Modification ◽  
Credit Risk Analysis

In this paper, a modified least squares support vector machine classifier, called the C-variable least squares support vector machine (C-VLSSVM) classifier, is proposed for credit risk analysis. The main idea of the proposed classifier is based on the prior knowledge that different classes may have different importance for modeling and more weight should be given to classes having more importance. The C-VLSSVM classifier can be obtained by a simple modification of the regularization parameter, based on the least squares support vector machine (LSSVM) classifier, whereby more weight is given to errors in classification of important classes, than to errors in classification of unimportant classes, while keeping the regularized terms in their original form. For illustration purpose, two real-world credit data sets are used to verify the effectiveness of the C-VLSSVM classifier. Experimental results obtained reveal that the proposed C-VLSSVM classifier can produce promising classification results in credit risk analysis, relative to other classifiers listed in this study.

scholarly journals Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 692
Author(s):  
Zhe Hua ◽  
Yancai Xiao ◽  
Jiadong Cao
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Frequency Domain ◽  
Wind Turbines ◽  
Development Trend ◽  
Fault Prediction ◽  
Support Vector ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Misalignment Fault

A misalignment fault is a kind of potential fault in double-fed wind turbines. The reasonable and effective fault prediction models are used to predict its development trend before serious faults occur, which can take measures to repair in advance and reduce human and material losses. In this paper, the Least Squares Support Vector Machine optimized by the Improved Artificial Fish Swarm Algorithm is used to predict the misalignment index of the experiment platform. The mixed features of time domain, frequency domain, and time-frequency domain indexes of vibration or stator current signals are the inputs of the Least Squares Support Vector Machine. The kurtosis of the same signals is the output of the model, and theprinciple of the normal distribution is adopted to set the warning line of misalignment fault. Compared with other optimization algorithms, the experimental results show that the proposed prediction model can predict the development trend of the misalignment index with the least prediction error.

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