scholarly journals Feature Mapping Techniques for Improving the Performance of Fault Diagnosis of Synchronous Generator

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
R. Gopinath ◽  
C. Santhosh Kumar ◽  
K. Vishnuprasad ◽  
K. I. Ramachandran
Keyword(s):  
Fault Diagnosis ◽  
Sparse Coding ◽  
Learning Algorithm ◽  
Dimensional Space ◽  
Synchronous Generator ◽  
Identification Accuracy ◽  
Support Vector ◽  
Feature Mapping ◽  
Data Set ◽  
Mapping Techniques

Support vector machine (SVM) is a popular machine learning algorithm used extensively in machine fault diagnosis. In this paper, linear, radial basis function (RBF), polynomial, and sigmoid kernels are experimented to diagnose inter-turn faults in a 3kVA synchronous generator. From the preliminary results, it is observed that the performance of the baseline systemis not satisfactory since the statistical features are nonlinear and does not match to the kernels used. In this work, the features are linearized to a higher dimensional space to improve the performance of fault diagnosis system for a synchronous generator using feature mapping techniques, sparse coding and locality constrained linear coding (LLC). Experiments and results show that LLC is superior to sparse coding for improving the performance of fault diagnosis of a synchronous generator. For the balanced data set, LLC improves the overall fault identification accuracy of the baseline RBF system by 22.56%, 18.43% and 17.05% for the R, Y and Bphase faults respectively.

scholarly journals Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis

2016 ◽  
Vol 8 (12) ◽  
pp. 168781401668308 ◽  
Author(s):  
Shuangyuan Wang ◽  
Yixiang Huang ◽  
Liang Gong ◽  
Lin Li ◽  
Chengliang Liu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Sparse Coding ◽  
Extraction Procedure ◽  
Vibration Signal ◽  
Support Vector ◽  
Data Set ◽  
Vibration Signals ◽  
Efficiency And Effectiveness ◽  
Gear Fault

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

scholarly journals Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

2020 ◽  
Vol 9 (3) ◽  
pp. 2758-2762
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Clinical Data ◽  
Disease Risk ◽  
Learning Algorithm ◽  
Information Storage ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set

A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.

Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs

GEOMATICA ◽  
2021 ◽  
pp. 1-23
Author(s):  
Roholah Yazdan ◽  
Masood Varshosaz ◽  
Saied Pirasteh ◽  
Fabio Remondino
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Geometric Constraints ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Traffic Signs ◽  
Deep Learning Algorithm ◽  
Comparison Results

Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided.

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.

Least Squares Support Vector Machine for Fault Diagnosis Optimization

2013 ◽  
Vol 347-350 ◽  
pp. 505-508
Author(s):  
Si Yang Liang ◽  
Jian Hong Lv
Keyword(s):  
Fault Diagnosis ◽  
Least Squares ◽  
Learning Algorithm ◽  
Test Sample ◽  
Digital Circuit ◽  
Training Sample ◽  
Support Vector ◽  
Vector Machines ◽  
Sample Data ◽  
Diagnosis Method

In order to improve the diagnostic accuracy of digital circuit, the fault diagnosis method based on support vector machines (SVM) is proposed. The input is fault characteristics of digital circuit; the output is the fault style. The connection of fault characteristics and style was established. Network learning algorithm using least squares, the training sample data is formed by the simulation, the test sample data is formed by the untrained simulation. The method achieved the classification of faulted digital circuits, and the results show that the method has the features of fast and high accuracy.

scholarly journals Rotating Machine Fault Diagnosis Based on Optimal Morphological Filter and Local Tangent Space Alignment

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Shaojiang Dong ◽  
Lili Chen ◽  
Baoping Tang ◽  
Xiangyang Xu ◽  
Zhengyuan Gao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Tangent Space ◽  
Learning Algorithm ◽  
Support Vector ◽  
Morphological Filter ◽  
Rotating Machine ◽  
Machine Fault Diagnosis ◽  
Mode Decomposition ◽  
Machine Fault ◽  
Local Tangent

In order to identify the fault of rotating machine effectively, a new method based on the morphological filter optimized by particle swarm optimization algorithm (PSO) and the nonlinear manifold learning algorithm local tangent space alignment (LTSA) is proposed. Firstly, the signal is purified by the morphological filter; the filter’s structure element (SE) is selected by PSO method. Then the filtered signals are decomposed by the empirical mode decomposition (EMD) method, and the extract features are mapped into the LTSA to extract the character features; then the support vector machine (SVM) model is used to achieve the rotating machine fault diagnosis. The proposed method is evaluated by vibration signals measured from bearings with faults. Results show that the method can effectively remove the noise and extract the fault features, so the rotating machine fault diagnosis can be achieved effectively.

Application of Support Vector Machine in Determination of Real Estate Price

2012 ◽  
Vol 461 ◽  
pp. 818-821
Author(s):  
Shi Hu Zhang
Keyword(s):  
Support Vector Machine ◽  
Real Estate ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Training Data ◽  
Small Samples ◽  
Support Vector ◽  
Data Set ◽  
Real Estate Price

The problem of real estate prices are the current focus of the community's concern. Support Vector Machine is a new machine learning algorithm, as its excellent performance of the study, and in small samples to identify many ways, and so has its unique advantages, is now used in many areas. Determination of real estate price is a complicated problem due to its non-linearity and the small quantity of training data. In this study, support vector machine (SVM) is proposed to forecast the price of real estate price in China. The experimental results indicate that the SVM method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. It was also found that the predictive ability of the SVM outperformed those of some traditional pattern recognition methods for the data set used here.

scholarly journals Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaochen Zhang ◽  
Dongxiang Jiang ◽  
Te Han ◽  
Nanfei Wang ◽  
Wenguang Yang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Imbalanced Data ◽  
Principal Component ◽  
Rotating Machinery ◽  
Support Vector ◽  
Data Set ◽  
Diagnosis Model ◽  
Sample Set

To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

Improved Learning Algorithm Based on Semi-Supervised Support Vector

2011 ◽  
Vol 474-476 ◽  
pp. 1-6
Author(s):  
Guo Xing Peng ◽  
Bei Li
Keyword(s):  
Support Vector Machines ◽  
Lower Bound ◽  
Simulation Analysis ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
Support Vector ◽  
Dual Function ◽  
Data Set ◽  
Vector Machines ◽  
Improved Learning

Improved learning algorithm for branch and bound for semi-supervised support vector machines is proposed, according to the greater difference in the optimal solution in different semi-supervised support vector machines for the same data set caused by the local optimization. The lower bound of node in IBBS3VM algorithm is re-defined, which will be pseudo-dual function value as the lower bound of node to avoid the large amount of calculation of 0-1 quadratic programming, reducing the lower bound of each node calculate the time complexity; at the same time, in determining the branch nodes, only based on the credibility of the unlabeled samples without the need to repeatedly carry out the training of support vector machines to enhance the training speed of the algorithm. Simulation analysis shows that IBBS3VM presented in this paper has faster training speed than BBS3VM algorithms, higher precision and stronger robustness than the other semi-supervised support vector machines.

scholarly journals Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jianwei Liu ◽  
Shuang Cheng Li ◽  
Xionglin Luo
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Adaptive Learning ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Support Vector ◽  
Data Set ◽  
Cancer Data ◽  
Public Data

Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data.L2regularization has been commonly used. If the training dataset contains many noise variables,L1regularization SVM will provide a better performance. However, bothL1andL2are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweightedp-norm regularization support vector machine for 0 <p≤ 2. A simulated data set was created to evaluate the algorithm. It was shown that apvalue of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using apvalue less thanL1norm. Moreover, we observe that the proposedLppenalty is more robust to noise variables than theL1andL2penalties.

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