scholarly journals Fault Diagnosis of Transmission Line using Feed Forward Neural Network

2019 ◽  
Vol 8 (9S2) ◽  
pp. 263-268
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transmission Lines ◽  
Field Work ◽  
Neural Nets ◽  
Feed Forward Neural Network ◽  
Training Time ◽  
Optimum Result ◽  
Human Effort ◽  
Selection Of

The implementation of neural network for the fault diagnosis is to improve the dependability of the proposed scheme by providing a more accurate, faster diagnosis relaying scheme as compared with the conventional relaying schemes. It is important to improve the relaying schemes regarding the shortcoming of the system and increase the dependability of the system by using the proposed relaying scheme. It also provide more accurate, faster relaying scheme. It also gives selective schemes as compared to conventional system. The techniques for survey employed some methods for the collection of data which involved a literature review of journals, from review on books, newspaper, magazines as well as field work, additional data was collected from researchers who are working in this field. To achieve optimum result we have to improve following things: (i) Training time, (ii) Selection of training vector, (iii) Upgrading of trained neural nets and integration of technologies. AI with its promise of adaptive training and generalization deserves scope. As a result we obtain a system which is more reliable, more accurate, and faster, has more dependability as well as it will selective according to the proposed relaying scheme as compare to the conventional relaying scheme. This system helps us to reduce the shortcoming like major faults which we faced in the complex system of transmission lines which will helps in reducing human effort, saves cost for maintaining the transmission system.

scholarly journals A Proposed Heuristic Optimization Algorithm for Detecting Network Attacks

2019 ◽  
Vol 2 (4) ◽  
pp. 530
Author(s):  
Amr Hassan Yassin ◽  
Hany Hamdy Hussien
Keyword(s):  
Neural Network ◽  
Heuristic Algorithms ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Network Attacks ◽  
Security Issues ◽  
Proposed Model ◽  
Network Intrusions ◽  
Attack Patterns ◽  
Selection Of

Due to the exponential growth of E-Business and computing capabilities over the web for a pay-for-use groundwork, the risk factors regarding security issues also increase rapidly. As the usage increases, it becomes very difficult to identify malicious attacks since the attack patterns change. Therefore, host machines in the network must continually be monitored for intrusions since they are the final endpoint of any network. The purpose of this work is to introduce a generalized neural network model that has the ability to detect network intrusions. Two recent heuristic algorithms inspired by the behavior of natural phenomena, namely, the particle swarm optimization (PSO) and gravitational search (GSA) algorithms are introduced. These algorithms are combined together to train a feed forward neural network (FNN) for the purpose of utilizing the effectiveness of these algorithms to reduce the problems of getting stuck in local minima and the time-consuming convergence rate. Dimension reduction focuses on using information obtained from NSL-KDD Cup 99 data set for the selection of some features to discover the type of attacks. Detecting the network attacks and the performance of the proposed model are evaluated under different patterns of network data.

An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks

2000 ◽  
Author(s):  
Pong-Jeu Lu ◽  
Ming-Chuan Zhang ◽  
Tzu-Cheng Hsu ◽  
Jin Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Success Rate ◽  
Neural Nets ◽  
Sensor Data ◽  
Trend Detection ◽  
Influence Coefficient ◽  
Data Validation ◽  
Engine Operation ◽  
Artificial Neural

Application of artificial neural network (ANN)-based method to perform engine condition monitoring and fault diagnosis is evaluated. Back-propagation, feedforward neural nets are employed for constructing engine diagnostic networks. Noise-contained training and testing data are generated using an influence coefficient matrix and the data scatters. The results indicate that under high-level noise conditions ANN fault diagnosis can only achieve a 50–60% success rate. For situations where sensor scatters are comparable to those of the normal engine operation, the success rates for both 4-input and 8-input ANN diagnoses achieve high scores which satisfy the minimum 90% requirement. It is surprising to find that the success rate of the 4-input diagnosis is almost as good as that of the 8-input. Although the ANN-based method possesses certain capability in resisting the influence of input noise, it is found that a preprocessor that can perform sensor data validation is of paramount importance. Auto-associative neural network (AANN) is introduced to reduce the noise level contained. It is shown that the noise can be greatly filtered to result in a higher success rate of diagnosis. This AANN data validation preprocessor can also serve as an instant trend detector which greatly improves the current smoothing methods in trend detection. It is concluded that ANN-based fault diagnostic method is of great potential for future use. However, further investigations using actual engine data have to be done to validate the present findings.

A Review of Neural Network Application for Fault Diagnosis of NPP

2009 ◽  
Author(s):  
He Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Nuclear Power ◽  
Layer Structure ◽  
Engineering Application ◽  
Model Performance ◽  
Training Sample ◽  
Feed Forward Neural Network ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Artificial Neural Network (ANN) with its self-learning capabilities, nonlinear mapping ability and generalization ability, has been widely applied for fault diagnosis of complex system like Nuclear Power Plant (NPP). In this paper, an overview of the application of supervised multi-layer feed-forward neural network for fault diagnosis of NPP is presented, including the following aspects: the acquisition of the training sample data, the determination of appropriate input and output data, the choice of hidden layer structure and the evaluation of network model performance. Finally, a number of key issues about the engineering application of neural network fault diagnosis in practice were discussed.

scholarly journals Application of Rough Set and Neural Network in Water Energy Utilization

2021 ◽  
Vol 9 ◽  
Author(s):  
Minghua Wei ◽  
Zhihong Zheng ◽  
Xiao Bai ◽  
Ji Lin ◽  
Farhad Taghizadeh-Hesary
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Unit ◽  
Rough Set ◽  
Hydraulic Turbine ◽  
Energy Utilization ◽  
Inference System ◽  
Training Time ◽  
Water Power ◽  
Artificial Neural

In water energy utilization, the damage of fault occurring in the power unit operational process to equipment directly affects the safety of the unit and efficiency of water power conversion and utilization, so fault diagnosis of water power unit equipment is especially important. This work combines a rough set and artificial neural network and uses it in fault diagnosis of hydraulic turbine conversion, puts forward rough set theory based on the tolerance relation and defines similarity relation between samples for the decision-making system whose attribute values are consecutive real numbers, and provides an attribute-reducing algorithm by making use of the condition that approximation classified quality will not change. The diagnostic rate of artificial neural networks based on a rough set is higher than that of the general three-layer back-propagation(BP) neural network, and the training time is also shortened. But, the network topology of an adaptive neural-fuzzy inference system is simpler than that of a neural network based on the rough set, the diagnostic accuracy is also higher, and the training time required under the same error condition is shorter. This algorithm processes consecutive failure data of the hydraulic turbine set, which has avoided data discretization, and this indicates that the algorithm is effective and reliable.

The Application of Support Vector Machine in Transformer Fault Diagnosis

2014 ◽  
Vol 926-930 ◽  
pp. 3410-3413
Author(s):  
Xiang Lin Tan ◽  
Chang Shu ◽  
Yun Feng Peng
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Model Selection ◽  
Basic Principle ◽  
Support Vector ◽  
Training Methods ◽  
Structure Selection ◽  
Transformer Fault ◽  
Selection Of

The basic principle, training methods and model selection of support vector machine (SVM) are expounded in this paper, and then we introduce SVM in transformer fault diagnosis which can overcome the problems of the structure selection of neural network.

Fault Diagnosis Method Based on KPCA and Selective Neural Network Ensemble

2014 ◽  
Vol 915-916 ◽  
pp. 1272-1276 ◽  
Author(s):  
Yan Yan Pang ◽  
Hai Ping Zhu ◽  
Fan Mao Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Binary Particle Swarm Optimization ◽  
Large Network ◽  
Neural Network Ensemble ◽  
Tennessee Eastman Process ◽  
Training Time ◽  
Diagnosis Method ◽  
Neural Network Algorithm ◽  
Bagging Method

Aiming at the problems of less study sample, large network scale and long training time existing in current fault diagnosis field, we develop a new method based on KPCA and selective neural network ensemble. First, reducing the data size by using KPCA to extract the sample features. Then achieving a selective neural network ensemble method based on improved binary particle swarm optimization algorithm (IBPSOSEN), and combining the two methods for fault diagnosis. In selective neural network algorithm, bagging method is used to take a number of different training sets of fault samples to solve the problem of less fault samples. Finally, simulation experiments and comparisons over Tennessee Eastman Process (TE) demonstrate the effectiveness and feasibility of the proposed method.

Contributions to Ranking an Ergonomic Workstation, Considering the Human Effort and the Microclimate Parameters, Using Neural Networks

2013 ◽  
Vol 371 ◽  
pp. 812-816 ◽  
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Experimental Results ◽  
Work Place ◽  
Feed Forward Neural Network ◽  
Linear Relationships ◽  
The Neural Network ◽  
Manufacture Industry ◽  
Human Effort ◽  
Hidden Layer

The paper presents a method to use a feed forward neural network in order to rank a working place from the manufacture industry. Neural networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. The neural network is simulated with a simple simulator: SSNN. In this paper, we considered as relevant for a work place ranking, 6 input parameters: temperature, humidity, noise, luminosity, load and frequency. The neural network designed for the study presented in this paper has 6 input neurons, 13 neurons in the hidden layer and 1 neuron in the output layer. We present also some experimental results obtained through simulations.

scholarly journals Transfer-Based Deep Neural Network for Fault Diagnosis of New Energy Vehicles

2021 ◽  
Vol 9 ◽  
Author(s):  
Yuping Wang ◽  
Weidong Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Rolling Bearing ◽  
Low Carbon ◽  
Training Time ◽  
Energy Applications ◽  
New Energy ◽  
New Energy Vehicles

New energy vehicles are crucial for low carbon applications of renewable energy and energy storage, while effective fault diagnostics of their rolling bearings is vital to ensure the vehicle’s safe and effective operations. To achieve satisfactory rolling bearing fault diagnosis of the new energy vehicle, a transfer-based deep neural network (DNN-TL) is proposed in this study by combining the benefits of both deep learning (DL) and transfer learning (TL). Specifically, by first constructing the convolutional neural networks (CNNs) and long short-term memory (LSTM) to preprocess vibration signals of new energy vehicles, the fault-related preliminary features could be extracted efficiently. Then, a grid search method called step heapsort is designed to optimize the hyperparameters of the constructed model. Afterward, both feature-based and model-based TLs are developed for the fault condition classifications transfer. Illustrative results show that the proposed DNN-TL method is able to recognize different faults accurately and robustly. Besides, the training time is significantly reduced to only 18s, while the accuracy is still over 95%. Due to the data-driven nature, the proposed DNN-TL could be applied to diagnose faults of new energy vehicles, further benefitting low carbon energy applications.

scholarly journals Fault diagnosis of roller bearings using selected classifiers

2018 ◽  
Vol 19 (12) ◽  
pp. 597-601
Author(s):  
Jakub Piekoszewski
Keyword(s):  
Neural Network ◽  
Roller Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Analysis Software ◽  
Feed Forward Neural Network ◽  
Roller Bearings ◽  
The University ◽  
Bearing Damage ◽  
Selection Of

Minor roller bearing damage may lead to serious failures of the de-vice. Thus, it is very important to detect such damage as early as possible to prevent further damage. This paper presents a selection of several theoretical tools from the field of artificial intelligence and their application in roller bearings fault classification. The considered tools are: k-nearest neighbour algorithm, decision tree, support vector machine, feed forward neural network (multilayer perceptron), Bayesian network and neural network with radial basis functions. All numerical experiments presented in the paper were performed with the use of real-world dataset and WEKA (Waikato Environment for Knowledge Analysis) software, available at the server of the University of Waikato.

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