Transformer Dissolved Gas Analysis Using Least Square Support Vector Machine and Bootstrap

Wenhu, Tang; Shintemirov, Almas; Wu, Q. H.

doi:10.1109/chicc.2006.4347139

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Performance comparisons were made between the combined GP-ANN, GP-support vector machine (GP-SVM) and GP-KNN classifiers and the ones derived from ANN, SVM and KNN classifiers, respectively. Wenhu et al [15] surveyed the accuracy rate of four techniques, namely, SVM, ANN, KNN and least square SVM (LS-SVM) for transformer fault classification. Bagheri et al [13] proposed fault gear identification and classification using ANN and KNN classifiers.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Moosavian

Ahmadi

Tabatabaeefar

et al. 2013

Shock and Vibration

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Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

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Section: Introductionmentioning

confidence: 99%

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Moosavian

Ahmadi

Tabatabaeefar

et al. 2013

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…We cannot chose but catch over more than one thousands fish, in a word, we can select different Numbers of VCN with different FM(Figures' Module), namely, 1000≤NVCN ＜ ≤1000+d, 0≤d 9000. Another example, the professor WU Wen-Hu who works at Tshinghua University of China gave out a difficult rewarded problem [9] of which was published in 1992 as follows.…”

Section: Roles In Dfs With Properties Of Ai-vcrmentioning

confidence: 99%

AI-VCR Searching without Information

Ye¹

2010

AMR

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This paper promotes an AI-VCR technology of searching without information. VCR is an extension of FCR(Fixed Carrying Rules) which is also computation rules of FCN(Fixed Carrying Numbers). The FCN are some special examples of VCN(Variable Carrying Numbers). DFS(Depthfirst Search) is a blind searching without information. In AI-VCR technology, AI(Artificial Intelligence) is used in dynamic designing computation rules of VCN. Both FCR and VCR in searching without information are variables of nodes’ depth function. Finally, it gives out some examples of problem-solving method, namely problem-solving such as catching fish with casting a net or herdsman picks peaches with his fierce-looking before climbing up a tree.

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“…Literature [9] takes oil chromatographic analysis, electrical Test and other multi-source information as fusion objects, transformer fault diagnosis model based on D-S evidence reasoning is proposed in this literature. Literature [10] comes up with the method of transformer fault diagnosis using information fusion algorithm based on least squares support vector machine combining DGA, which significantly improve the classification precision of the transformer fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Research on Transformer Fault Diagnosis based on Multi-source Information Fusion

Wang¹,

Wu²,

Xu³

2014

IJCA

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DGA (Dissolved Gas Analysis) is the traditional transformer fault diagnosis method, but it mainly depends on the experience of operators. In order to solve the limitations of traditional method, this paper introduces intelligent method for fault diagnosis of transformer. The intelligent method made fusion of various data, including SCADA data, oil dissolved gas sensor data, related electrical test data, operation maintenance records, and so on, employed space-time weighting fusion method based on BP neural network, and put forward the model of transformer fault diagnosis based on multi-source information fusion, which improved the accuracy of the transformer fault diagnosis dramatically.

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Transformer Dissolved Gas Analysis Using Least Square Support Vector Machine and Bootstrap

Cited by 6 publications

References 11 publications

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

AI-VCR Searching without Information

Research on Transformer Fault Diagnosis based on Multi-source Information Fusion

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