scholarly journals Fault Diagnosis Method of DC Charging Points for EVs Based on Deep Belief Network

2021 ◽  
Vol 12 (1) ◽  
pp. 47
Author(s):  
Dexin Gao ◽  
Xihao Lin
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Deep Belief Network ◽  
Fine Tuning ◽  
Convergence Time ◽  
Support Vector ◽  
Actual Measurement ◽  
Belief Network ◽  
Diagnosis Method

According to the complex fault mechanism of direct current (DC) charging points for electric vehicles (EVs) and the poor application effect of traditional fault diagnosis methods, a new kind of fault diagnosis method for DC charging points for EVs based on deep belief network (DBN) is proposed, which combines the advantages of DBN in feature extraction and processing nonlinear data. This method utilizes the actual measurement data of the charging points to realize the unsupervised feature extraction and parameter fine-tuning of the network, and builds the deep network model to complete the accurate fault diagnosis of the charging points. The effectiveness of this method is examined by comparing with the backpropagation neural network, radial basis function neural network, support vector machine, and convolutional neural network in terms of accuracy and model convergence time. The experimental results prove that the proposed method has a higher fault diagnosis accuracy than the above fault diagnosis methods.

scholarly journals Analog Circuit Fault Diagnosis Based on Adaptive Gaussian Deep Belief Network

2018 ◽  
Vol 173 ◽  
pp. 03090
Author(s):  
WANG Ying-chen ◽  
DUAN Xiu-sheng
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Analog Circuit ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Network Application ◽  
Circuit Fault Diagnosis

Aiming at the problem that the traditional intelligent fault diagnosis method is overly dependent on feature extraction and the lack of generalization ability, deep belief network is proposed for the fault diagnosis of the analog circuit; Then, by analyzing the deficiency of deep belief network application, a Gaussian deep belief network based on adaptive learning rate is proposed. The automatic adjustment learning step is adopted to further improve fault diagnosis efficiency and diagnosis accuracy; Finally, particle swarm support vector machine to extract the fault characteristics to identify. The simulation results of circuit fault diagnosis show that the algorithm has faster convergence speed and higher fault diagnosis accuracy.

Fault diagnosis method of rolling bearing based on deep belief network

2018 ◽  
Vol 32 (11) ◽  
pp. 5139-5145 ◽  
Author(s):  
Zhiwu Shang ◽  
Xiangxiang Liao ◽  
Rui Geng ◽  
Maosheng Gao ◽  
Xia Liu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Deep Belief Network ◽  
Belief Network ◽  
Diagnosis Method

A rolling bearing fault diagnosis method based on fastDTW and an AGBDBN

2020 ◽  
Vol 62 (8) ◽  
pp. 457-463 ◽  
Author(s):  
Shang Zhiwu ◽  
Liu Xia ◽  
Li Wanxiang ◽  
Gao Maosheng ◽  
Yu Yan
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Deep Belief Network ◽  
Residual Vector ◽  
Belief Network ◽  
Network Training ◽  
Diagnosis Method ◽  
Stationary Vibration ◽  
Dynamic Time

In order to improve fault feature extraction and diagnosis for rolling bearings, a fault diagnosis method based on fast dynamic time warping (fastDTW) and an adaptive Gaussian-Bernoulli deep belief network (AGBDBN) is proposed in this paper. Firstly, for the non-stationary vibration signal characteristics of the bearing, the fastDTW algorithm is used to calculate the residual vector of the fault signal, thereby enhancing the fault characteristic information. Then, according to the continuous vibration value of the bearing vibration signal, a standard deep belief network (DBN) is improved to deal with the problem that the optimal setting for the learning rate is difficult to achieve in the deep neural network training process and the AGBDBN model is used for fault diagnosis. Finally, the proposed method is compared with a variety of model diagnosis methods. The experimental results show that the proposed method achieved good diagnostic results.

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 203-207
Author(s):  
Jian Hua Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Electrical Control ◽  
Diagnosis Method ◽  
The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

scholarly journals Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8498
Author(s):  
Lei Yang ◽  
Chunqing Zhao ◽  
Chao Lu ◽  
Lianzhen Wei ◽  
Jianwei Gong
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Back Propagation ◽  
Front Wheel ◽  
Driving Behavior ◽  
Deep Belief Network ◽  
Intelligent Vehicles ◽  
Support Vector ◽  
Safe Driving ◽  
Belief Network

Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-target sigmoid regression (MSR) layer with DBN to predict the front wheel angle and speed of the ego vehicle. Precisely, the MSR-DBN consists of two sub-networks: one is for the front wheel angle, and the other one is for speed. This MSR-DBN model allows ones to optimize lateral and longitudinal behavior predictions through a systematic testing method. In addition, we consider the historical states of the ego vehicle and surrounding vehicles and the driver’s operations as inputs to predict driving behaviors in a real-world environment. Comparison of the prediction results of MSR-DBN with a general DBN model, back propagation (BP) neural network, support vector regression (SVR), and radical basis function (RBF) neural network, demonstrates that the proposed MSR-DBN outperforms the others in terms of accuracy and robustness.

scholarly journals Fault Signal Recognition in Power Distribution System using Deep Belief Network

2018 ◽  
Vol 29 (1) ◽  
pp. 459-474
Author(s):  
T.C. Srinivasa Rao ◽  
S.S. Tulasi Ram ◽  
J.B.V. Subrahmanyam
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Power Distribution ◽  
Distribution System ◽  
Electrical Power ◽  
Deep Belief Network ◽  
Support Vector ◽  
Neural Network Models ◽  
Power Distribution System ◽  
Belief Network

Abstract Nowadays, electrical power system is considered as one of the most complicated artificial systems all over the globe, as social and economic development depends on intact, consistent, stable and economic functions. Owing to diverse random causes, accidental failures occur in electrical power systems. Considering this issue, this article aimed to propose the use of deep belief network (DBN) in detecting and classifying fault signals such as transient, sag and swell in the transmission line. Here, wavelet-decomposed fault signals are extracted and the fault is diagnosed based on the decomposed signal by the DBN model. Further, this article provides the performance analysis by determining the types I and II measures and root-mean-square-error (RMSE) measure. In the performance analysis, it compares the performance of the DBN model to various conventional models like linear support vector machine (SVM), quadratic SVM, radial basis function SVM, polynomial SVM, multilayer perceptron SVM, Levenberg-Marquardt neural network and gradient descent neural network models. The simulation results validate that the proposed DBN model effectively detects and classifies the fault signal in power distribution system when compared to the traditional model.

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