Intelligent Bearing Diagnostics Using Wavelet Support Vector Machine

2014 ◽  
Vol 493 ◽  
pp. 337-342 ◽  
Author(s):  
Achmad Widodo ◽  
I. Haryanto ◽  
T. Prahasto
Keyword(s):  
Support Vector Machine ◽  
Input Data ◽  
Intelligent System ◽  
Feature Space ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Fault Diagnostics ◽  
Standard Data ◽  
Rolling Element

This paper deals with implementation of intelligent system for fault diagnostics of rolling element bearing. In this work, the proposed intelligent system was basically created using support vector machine (SVM) due to its excellent performance in classification task. Moreover, SVM was modified by introducing wavelet function as kernel for mapping input data into feature space. Input data were vibration signals acquired from bearings through standard data acquisition process. Statistical features were then calculated from bearing signals, and extraction of salient features was conducted using component analysis. Results of fault diagnostics are shown by observing classification of bearing conditions which gives plausible accuracy in testing of the proposed system.

Vibration Gear Fault Diagnostics Technique Using Wavelet Support Vector Machine

2014 ◽  
Vol 564 ◽  
pp. 182-188
Author(s):  
Achmad Widodo ◽  
D.P. Dewi Widowati ◽  
D. Satrijo ◽  
I. Haryanto
Keyword(s):  
Support Vector Machine ◽  
Input Data ◽  
Feature Space ◽  
Testing Procedure ◽  
Haar Wavelet ◽  
Support Vector ◽  
Fault Diagnostics ◽  
Test Rig ◽  
Vibration Data ◽  
Gear Fault

Intelligent diagnostics tool for detecting damaged bevel gears was developed based on wavelet support vector machine (WSVM). In this technique, the existing method of SVM was modified by introducing Haar wavelet function as kernel for mapping input data into feature space. The developed method was experimentally evaluated by vibration data measured from test rig machinery fault simulator (MFS). There were four conditions of gears namely normal, worn, teeth defect and one missing-teeth which has been experimented. Statistical features were then calculated from vibration signals and they were employed as input data for training WSVM. Fault diagnostics of bevel gear was performed by executing classification task in trained WSVM. The accuracy of fault diagnostics were evaluated by testing procedure through vibration data acquired from test rig. The results show that the proposed system gives plausible performance in fault diagnostics based on experimental work.

scholarly journals Rough Support Vector Machine for Classification with Interval and Incomplete Data

2020 ◽  
Vol 10 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Robert K. Nowicki ◽  
Konrad Grzanek ◽  
Yoichi Hayashi
Keyword(s):  
Support Vector Machine ◽  
Rough Sets ◽  
Incomplete Data ◽  
Input Data ◽  
Missing Values ◽  
Classification Scheme ◽  
Equivalence Classes ◽  
Support Vector ◽  
Positive Boundary

AbstractThe paper presents the idea of connecting the concepts of the Vapnik’s support vector machine with Pawlak’s rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three–way decision. The proposed solution will be tested using several popular benchmarks.

Fault Diagnosis of Metallurgical Machinery Based on Spectral Kurtosis and GA-SVM

2013 ◽  
Vol 634-638 ◽  
pp. 3958-3961 ◽  
Author(s):  
Yun Li ◽  
Yan Gao ◽  
Jun Guo ◽  
Xian Jun Yu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Simulation Study ◽  
High Accuracy ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Spectral Kurtosis ◽  
Rolling Element ◽  
Element Bearing

This paper proposed a new method of rolling element bearing (REB) fault diagnosis for metallurgical machinery. Mainly it stresses on the combination of spectral kurtosis (SK) and supports vector machine (SVM), using genetic algorithm (GA) to optimize the parameters of support vector machine at the same time. Thus, this study aims to integrate SK, GA and SVM in order to develop an intelligent REB fault detector for metallurgical machineries. Simulation study indicates that this method can effectively detect the REB faults with a high accuracy.

scholarly journals Selection of Specialization Class Using Support Vector Machine (SVM) Method in Sekolah Menengah Atas Negeri 1 Ambon

CAUCHY ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 162-168
Author(s):  
Stevanny Tamaela ◽  
Yopi Andry Lesnussa ◽  
Venn Yan Ishak Ilwaru
Keyword(s):  
Support Vector Machine ◽  
Input Data ◽  
School Level ◽  
Support Vector ◽  
Student Selection ◽  
Accuracy Rate ◽  
High School Level ◽  
Decision Making System ◽  
Selection Of

The curriculum is a plan to form the abilities and character of children based on a standard. One of its form is the division of specialization classes at the high school level. The 2013 curriculum emphasizes that all students in Indonesia can practice their abilities based on their interests and talents, so students no longer choose majors but choose abilities (interests) in them specialize. This research uses the Support Vector Machine (SVM) method in specialization Decision Making System (DMS) at SMA Negeri 1 Ambon. By using the motivating acceptance factors and student selection as input data, this SVM method that processed with MATLAB Software produces a Classification of Interest Class with an accuracy rate more than 95%.

scholarly journals LAND COVER CLASSIFICATION OF ALOS PALSAR DATA USING SUPPORT VECTOR MACHINE

2014 ◽  
Vol 10 (1) ◽  
Author(s):  
Katmoko Ari Sambodo ◽  
Novie Indriasari
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Feature Space ◽  
Land Cover Classification ◽  
Support Vector ◽  
Rubber Plantation ◽  
Alos Palsar ◽  
Training Samples ◽  
Separating Hyperplane

Land cover classification is  one  of  the  extensive  used  applications in  the  field  of remote sensing. Recently, Synthetic Aperture Radar (SAR) data has become an increasing popular data source because  its  capability  to  penetrate  through  clouds,  haze,  and  smoke.  This  study  showed  on  an alternative  method  for  land  cover  classification  of  ALOS-PALSAR  data  using  Support  Vector Machine (SVM) classifier. SVM discriminates two classes by fitting an optimal separating hyperplane to the training data in a multidimensional feature space, by using only the closest training samples. In order  to  minimize  the  presence  of  outliers  in  the  training  samples  and  to  increase  inter-class separabilities,  prior  to  classification,  a  training  sample  selection  and  evaluation  technique  by identifying its position in a horizontal vertical–vertical horizontal polarization (HV-HH) feature space was applied. The effectiveness of our method was demonstrated using ALOS PALSAR data (25 m mosaic, dual polarization) acquired in Jambi and South Sumatra, Indonesia. There were nine different classes  discriminated:  forest,  rubber  plantation,  mangrove  &  shrubs  with  trees,  oilpalm  &  coconut, shrubs,  cropland,  bare  soil,  settlement,  and  water.  Overall  accuracy  of  87.79%  was  obtained,  with producer’s accuracies for forest, rubber plantation, mangrove & shrubs with trees, cropland, and water class were greater than 92%.

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