Health Monitoring of Gears Based on Vibrations by Support Vector Machine Algorithms

2012 ◽  
Author(s):  
D. J. Bordoloi ◽  
Rajiv Tiwari
Keyword(s):  
Sensitivity Analysis ◽  
Support Vector Machine ◽  
Health Monitoring ◽  
Time Domain ◽  
Training Data ◽  
Support Vector ◽  
Vibration Signals ◽  
Time Domain Data ◽  
Testing Data ◽  
Speed Range

Health monitoring of gears is very critical for satisfactorily overall working of the complex machinery. Thus, the ability to detect gear faults and classify them based on their nature becomes very important aspect of health monitoring of machines. In this paper, SVM algorithms have been used for the multiclass prediction of faults with the help of time domain vibration signals obtained from the gearbox casing operated in a suitable speed range. Moreover, it tries to examine the performance of the SVM technique by optimizing its parameters on utilization of time domain data from multi-fault gear box. The SVM software was fed with the training data and testing data at similar operating speeds for three types of defects and no defect case, and classification ability of SVM was noted and found to be excellent. The sensitivity analysis of optimized parameters is studied and conclusions are drawn.

Classification Method of Hand Gestures Based on Support Vector Machine

2018 ◽  
Vol 7 (3) ◽  
Author(s):  
Wahyu Caesarendra
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Time Domain ◽  
Principal Component ◽  
Training Data ◽  
Signal Classification ◽  
Support Vector ◽  
Hand Gestures ◽  
Emg Signal ◽  
Testing Data

This paper presents the EMG signal classification based on PCA and SVM method. The data is acquired from the 5 subjects and each subject perform 7 hand gestures includes the tripod, power, precision closed, finger point, mouse, hand open, and hand close. Each gesture is repeated 10 times (5 data as training data and the 5 remaining data as testing data). Each of training and testing data are processed using 16 features extraction in time–domain and reduced using principal component analysis (PCA) to obtain new set of features. Features classification using support vector machine classify new set of features from each subject result 85% - 89% percentage of training classification. Training data classification is tested using testing data of EMG signals and giving accuracy reach 80% - 86%.

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 Pattern recognition of rigid hoist guides based on support vector machine

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881230
Author(s):  
Yansong Ma ◽  
Jiannan Yao ◽  
Chi Ma ◽  
Xingming Xiao
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Training Data ◽  
Support Vector ◽  
Characteristic Parameters ◽  
Test Rig ◽  
Numerical Result ◽  
Testing Data

In order to evaluate the serviceable condition of rigid hoist guides and recognize their status patterns, vibration and angle signals were collected from an test-rig which can simulate different sorts of defects. Characteristic parameters by which status patterns can be judged were selected and designed. Multi-classifier based on support vector machine was applied during recognition. The collected samples were divided into two parts which were used as training data and testing data, respectively. The numerical result verified the validity of support vector machine multi-classifier on pattern recognition of rigid hoist guides.

Health Monitoring of Gear Elements Based on Time-Frequency Vibration by Support Vector Machine Algorithms

2013 ◽  
Author(s):  
D. J. Bordoloi ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Health Monitoring ◽  
Time Domain ◽  
Wavelet Packet ◽  
Optimization Methods ◽  
Fault Classification ◽  
Support Vector ◽  
Time Frequency ◽  
Bee Colony

Health monitoring of a gear box has been attempted by the support vector machine (SVM) learning technique with the help of time-frequency (wavelet) vibration data. Multi-fault classification capability of the SVM is suitably demonstrated that is based on the selection of SVM parameters. Different optimization methods (i.e., the grid-search method (GSM), the genetic algorithm (GA) and the artificial bee colony algorithm (ABCA)) have been performed for optimizing the SVM parameters. Four fault conditions have been considered including the no defect case. Time domain vibration signals were obtained from the gearbox casing operated in a suitable speed range. The continuous wavelet transform (CWT) and wavelet packet transform (WPT) are extracted from time domain signals. A set of statistical features are extracted from the wavelet transform. The classification ability is noted and compared against predictions when purely time domain data is used, and it shows an excellent prediction performance.

Unbalance detection in rotating machinery based on support vector machine using time and frequency domain vibration features

2021 ◽  
pp. 095745652199983
Author(s):  
Purushottam Gangsar ◽  
Rohit Kumar Pandey ◽  
Manoj Chouksey
Keyword(s):  
Support Vector Machine ◽  
Frequency Domain ◽  
Time Domain ◽  
Diagnostic System ◽  
Support Vector ◽  
Rotating System ◽  
Artificial Intelligence Technique ◽  
Vibration Signals ◽  
Unbalanced Rotor ◽  
The Time Domain

The automated diagnostics of the unbalance in a rotor system has been presented in this study based on an artificial intelligence technique called support vector machine. In order to develop a support vector machine–based unbalance diagnosis, first the raw vibration signals in time and frequency domain are measured experimentally from healthy and unbalanced rotor installed on machine fault simulator. Then, three critical statistical features, namely, standard deviation, skewness, and kurtosis are extracted from the time and frequency domain vibration signals. Further, the features are used for training and testing of the support vector machine for building the automated diagnostic system for unbalance in a rotating system. The results from the present study show that the unbalance fault diagnosis can be effectively done based on the developed support vector machine–based methodology. The automated diagnosis of unbalance is possible with the time domain as well as frequency domain features. The results are better with time domain features than frequency domain features. In addition, the diagnosis is performed and found to be robust at most of the operating speeds of the rotor; however, the diagnosis should be avoided to attempt using the present methodology at very lower operating speeds.

Klasifikasi Anjing dan Kucing menggunakan Algoritma Linear Discriminant Analysis dan Support Vector Machine

2019 ◽  
Vol 11 (1) ◽  
pp. 46-51
Author(s):  
Alethea Suryadibrata ◽  
Suryadi Darmawan Salim
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Classification Systems ◽  
Training Data ◽  
Support Vector ◽  
Linear Discriminant ◽  
Medical Needs ◽  
Testing Data

One of the factors driving technological development is the increase in computers ability to complete various jobs. One of them is doing image processing, which is widely used in our daily life, such as the use of fingerprints, face/iris recognition barcodes, medical needs, and various other uses. Classification is one of the applications of image processing that is used the most. One algorithm that can be used for the development of image classification systems is Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). LDA is a feature extraction algorithm to find a subspace that separates classes well. SVM is a classification algorithm, based on the idea of finding a hyperplane that best divides a dataset into classes. In this study, LDA and SVM algorithms were tested on the dog and cat classification system, with the highest F- score calculation results being 0.69 with 200 training data and 50 testing data for cats and 0.64 with 200 training data and 30 testing data for dogs.

scholarly journals Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results

2021 ◽  
Vol 4 (1) ◽  
pp. 22-27
Author(s):  
Saikin Saikin ◽  
◽  
Sofiansyah Fadli ◽  
Maulana Ashari ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Training Data ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Validation Data ◽  
Machine Method ◽  
Testing Data ◽  
The Impact

The performance of the organizations or companiesare based on the qualities possessed by their employee. Both of good or bad employee performance will have an impact on productivity and the impact of profits obtained by the company. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and can solve high non-linearity, regression, etc. In machine learning, the optimization model is a part for improving the accuracy of the model for data learning. Several techniques are used, one of which is feature selection, namely reducing data dimensions so that it can reduce computation in data modeling. This study aims to apply the method of machine learning to the employee data of the Bank Rakyat Indonesia (BRI) company. The method used is SVM method by increasing the accuracy of learning data by using a feature selection technique using a wrapper algorithm. From the results of the classification test, the average accuracy obtained is 72 percent with a precision value of 71 and the recall value is rounded off to 72 percent, with a combination of SVM and cross-validation. Data obtained from Kaggle data, which consists of training data and testing data. each consisting of 30 columns and 22005 rows in the training data and testing data consisting of 29 col-umns and 6000 rows. The results of this study get a classification score of 82 percent. The precision value obtained is rounded off to 82 percent, a recall of 86 percent and an f1-score of 81 percent.

Wavelet Packet and Support Vector Machine for Engine Fault Diagnosis

2011 ◽  
Vol 230-232 ◽  
pp. 1-6
Author(s):  
Li Li ◽  
Ji Li ◽  
Bao Jia Chen
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Gasoline Engine ◽  
Wavelet Packet ◽  
Training Data ◽  
Combination Method ◽  
Support Vector ◽  
Combined Method ◽  
Vibration Signals ◽  
Engine Vibration

Aimed at the complexity of engine vibration, the paper proposed a combination method of wavelet packet and support vector machine for engine fault diagnosis based on the vibration signals. The vibration signals were collected from a gasoline engine, which type is Dongfeng EQ6100 (Chinese engine). The signals cover four working conditions, i.e. normal, piston knocking, piston pin fault, crankshaft bearing fault, under two engine conditions of on- and off-ignition, respectively. Firstly, wavelet packet was used to extract the features of the signals. Then, the off-ignition signals were selected to be the training data to construct a multi-class classifier based on support vector machine (SVM). Finally, applied the classifier to the engine diagnosis, and the faults were recognized effectively. The results demonstrate that the combined method is suitable to diagnose engine faults, especially for small signal samples.

An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

2020 ◽  
Vol 13 ◽  
Author(s):  
Jianfeng Jiang
Keyword(s):  
Fault Diagnosis ◽  
Fractal Analysis ◽  
Analog Circuit ◽  
Training Data ◽  
Support Vector ◽  
Pass Filter ◽  
Multiple Kernel ◽  
Testing Data ◽  
Circuit Fault Diagnosis ◽  
Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

scholarly journals Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods

2019 ◽  
Vol 6 (5) ◽  
pp. 190001 ◽  
Author(s):  
Katherine E. Klug ◽  
Christian M. Jennings ◽  
Nicholas Lytal ◽  
Lingling An ◽  
Jeong-Yeol Yoon
Keyword(s):  
Heavy Metal ◽  
Support Vector Machine ◽  
Metal Ions ◽  
Heavy Metal Ions ◽  
Mie Scattering ◽  
Training Data ◽  
Scattering Data ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Analysis

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.

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