scholarly journals A Convolutional Neural Network-Based Model for Multi-Source and Single-Source Partial Discharge Pattern Classification Using Only Single-Source Training Set

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1355
Author(s):  
Sara Mantach ◽  
Ahmed Ashraf ◽  
Hamed Janani ◽  
Behzad Kordi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Standard Procedure ◽  
Partial Discharge ◽  
Partial Discharges ◽  
Single Source ◽  
Multiple Sources ◽  
Average Percentage ◽  
The Status ◽  
Single Branch

Classification of the sources of partial discharges has been a standard procedure to assess the status of insulation in high voltage systems. One of the challenges while classifying these sources is the decision on the distinct properties of each one, often requiring the skills of trained human experts. Machine learning offers a solution to this problem by allowing to train models based on extracted features. The performance of such algorithms heavily depends on the choice of features. This can be overcome by using deep learning where feature extraction is done automatically by the algorithm, and the input to such an algorithm is the raw input data. In this work, an enhanced convolutional neural network is proposed that is capable of classifying single sources as well as multiple sources of partial discharges without introducing multiple sources in the training phase. The training is done by using only single-source phase-resolved partial discharge (PRPD) patterns, while testing is performed on both single and multi-source PRPD patterns. The proposed model is compared with single-branch CNN architecture. The average percentage improvements of the proposed architecture for single-source PDs and multi-source PDs are 99.6% and 96.7% respectively, compared to 96.2% and 77.3% for that of the traditional single-branch CNN architecture.

scholarly journals Convolutional-Neural-Network-Based Partial Discharge Diagnosis for Power Transformer Using UHF Sensor

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 207377-207388
Author(s):  
The-Duong Do ◽  
Vo-Nguyen Tuyet-Doan ◽  
Yong-Sung Cho ◽  
Jong-Ho Sun ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Power Transformer ◽  
Partial Discharge ◽  
Discharge Diagnosis

scholarly journals Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
S. Venkatesh ◽  
S. Gopal ◽  
K. Kannan
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Partial Discharge ◽  
Clustering Algorithms ◽  
Research Work ◽  
Experimental Studies ◽  
Multiple Sources ◽  
Graph Theoretic ◽  
Large Sets ◽  
Novel Approach

Partial discharge (PD) is a major cause of failure of power apparatus and hence its measurement and analysis have emerged as a vital field in assessing the condition of the insulation system. Several efforts have been undertaken by researchers to classify PD pulses utilizing artificial intelligence techniques. Recently, the focus has shifted to the identification of multiple sources of PD since it is often encountered in real-time measurements. Studies have indicated that classification of multi-source PD becomes difficult with the degree of overlap and that several techniques such as mixed Weibull functions, neural networks, and wavelet transformation have been attempted with limited success. Since digital PD acquisition systems record data for a substantial period, the database becomes large, posing considerable difficulties during classification. This research work aims firstly at analyzing aspects concerning classification capability during the discrimination of multisource PD patterns. Secondly, it attempts at extending the previous work of the authors in utilizing the novel approach of probabilistic neural network versions for classifying moderate sets of PD sources to that of large sets. The third focus is on comparing the ability of partition-based algorithms, namely, the labelled (learning vector quantization) and unlabelled (K-means) versions, with that of a novel hypergraph-based clustering method in providing parsimonious sets of centers during classification.

scholarly journals Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3539 ◽  
Author(s):  
Chang-Cheng Lo ◽  
Ching-Hung Lee ◽  
Wen-Cheng Huang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Loss Function ◽  
Feature Space ◽  
Vibration Signals ◽  
One Dimensional ◽  
Experimental Platform ◽  
Proposed Model ◽  
The Status ◽  
Gear Mechanism

This study aimed to propose a prognostic method based on a one-dimensional convolutional neural network (1-D CNN) with clustering loss by classification training. The 1-D CNN was trained by collecting the vibration signals of normal and malfunction data in hybrid loss function (i.e., classification loss in output and clustering loss in feature space). Subsequently, the obtained feature was adopted to estimate the status for prognosis. The open bearing dataset and established gear platform were utilized to validate the functionality and feasibility of the proposed model. Moreover, the experimental platform was used to simulate the gear mechanism of the semiconductor robot to conduct a practical experiment to verify the accuracy of the model estimation. The experimental results demonstrate the performance and effectiveness of the proposed method.

scholarly journals GIS Partial Discharge Patterns Recognition with Spherical Convolutional Neural Network

2020 ◽  
Vol 853 ◽  
pp. 012001
Author(s):  
Wei Yang ◽  
Guobao Zhang ◽  
Taiyun Zhu ◽  
Mengyi Cai ◽  
Hengyang Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Partial Discharge ◽  
Discharge Patterns

An Enhanced Recurrent Convolutional Neural Network for Predicting the Status Stage of Patients with Chronic obstructive Pulmonary Diseases: Method Design (Preprint)

2021 ◽  
Author(s):  
Wei Li ◽  
Tianyong Hao ◽  
Zhanjie Mai ◽  
Pengjiu Yu ◽  
Chunli Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real World ◽  
The State ◽  
Disease Stage ◽  
Chronic Obstructive ◽  
Clinical Dataset ◽  
Copd Patients ◽  
Proposed Model ◽  
The Status

BACKGROUND Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of death in China and has caused serious affect to health and life quality. However, the status stage of a patient is difficult to be accurately assessed because of dynamic changes in the condition and complex risk factors. A rapid and accurate methods to predict disease stage of COPD patients is of great significance. OBJECTIVE This study aims to explore an enhanced recurrent convolutional neural networks model for predicting correct staging of patients with COPD in China for assistant disease prevention and treatment. METHODS Data was collected from The First Affiliate Hospital of Guangzhou Medical University, which had standardized disease registration and follow-up management for 5108 patients with COPD. Our enhanced recurrent convolutional neural network consists of a bidirectional LSTM layer, a convolutional layer, a max-pooling layer, and an output layer. RESULTS The model proposed was evaluated on the real-world clinical dataset of 5108 COPD patients to predict the state stage of the disease. The performance of the proposed model achieved 93.2% in terms of accuracy, outperforming a list of baseline models. CONCLUSIONS This paper proposes an enhanced recurrent convolutional neural network model which is experimented on a real-world clinical dataset containing around 5,000 patients with COPD. The proposed model achieves the best performance on all evaluation metrics indicating its feasibility in predicting the state stage of diseases.

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