Artificial neural network based detection of neutral relay defects

The problem considered in the work is concerned to the automatic detecting and identifying defects in a neutral relay. The special design of electromechanical neutral relays is responsible for the strong asymmetry of its output signal for all possible safety-critical influences, and therefore neutral relays have negligible values of dangerous failures rate. To ensure the safe operation of relay-based train control systems, electromechanical relays should be periodically subjected to routine maintenance, during which their main operating parameters are measured, and the relays are set up in accordance with technical regulations. These measurements are mainly done manually, so they take a lot of time (up to four hours per relay), are expensive, and the results are subjective. In recent years, fault diagnosis methods based on artificial neural networks (ANN) have received considerable attention. The ANN-based classification of relay defects using the time dependence of the transient current in the relay coil during its switching is very promising for practical utilization, but for efficient use of ANN a lot of data is required to train the artificial neural network. To reduce the ANN training time, a pre-processing of the time dependence of relay transient current was proposed using wavelet transform and wavelet energy entropy, which makes it possible to reveal the features of the main defects of the relay armature, contact springs, and magnetic system. The effectiveness of the proposed approach for automatic detecting and identifying of the neutral relays defects was confirmed during testing of the relays with various artificially created defects.

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Tool Condition Monitoring Using Artificial Neural Network Models

10.4018/978-1-6684-2408-7.ch019 ◽

2022 ◽

pp. 400-426

Author(s):

Srinivasa P. Pai ◽

Nagabhushana T. N.

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Condition Monitoring ◽

Medium Carbon Steel ◽

Tool Condition Monitoring ◽

Training Time ◽

Tool Condition ◽

Cell Structures ◽

Artificial Neural ◽

Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

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Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network

Mathematical Problems in Engineering ◽

10.1155/2013/346949 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

R. Manjula Devi ◽

S. Kuppuswami ◽

R. C. Suganthe

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Training Model ◽

Training Data ◽

Experimental Result ◽

Training Algorithms ◽

Data Set ◽

Training Time ◽

Input Sample ◽

Artificial Neural

Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do not categorize perfectly in the previous epoch which dynamically reducing the number of input samples exhibited to the network at every single epoch without affecting the network’s accuracy. Thus decreasing the size of the training set can reduce the training time, thereby ameliorating the training speed. This LAST algorithm also determines how many epochs the particular input sample has to skip depending upon the successful classification of that input sample. This LAST algorithm can be incorporated into any supervised training algorithms. Experimental result shows that the training speed attained by LAST algorithm is preferably higher than that of other conventional training algorithms.

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Predicting Model in the Smelting Magnesium by Silicon-Thermo-Reduction Based on Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.146-147.571 ◽

2010 ◽

Vol 146-147 ◽

pp. 571-574

Author(s):

Liang Bo Ji ◽

Yong Zhi Li

Keyword(s):

Neural Network ◽

Neural Networks ◽

Mathematical Model ◽

Artificial Neural Network ◽

Process Parameters ◽

Effective Means ◽

Limited Liability ◽

Predicting Model ◽

Artificial Neural ◽

Set Up

This paper described the application of neural networks in predicting the rate of producing magnesium by silicon-thermo-reduction. Fir st of all, a mathematical model between the process parameters and the the rate of producing magnesium was set up with neural network. When the model was satisfied, it could be used for predicting the rate of producing magnesium. Through doing a great number of productive tests in the winca(hebi) magnesium company with limited liability according to the satisfied model, the rate of the producing magnesium is increasing obviously. So it is a kind of effective means for increasing producing magnesium by silicon-thermo-reduction.

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Identifying Damage of the Benchmark Structure by Using Artificial Neural Network Methods

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.219-220.312 ◽

2011 ◽

Vol 219-220 ◽

pp. 312-317 ◽

Cited By ~ 2

Author(s):

Bai Sheng Wang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Damage Identification ◽

Damage Localization ◽

Building Structures ◽

Bp Network ◽

Network Methods ◽

Damage Extent ◽

Artificial Neural ◽

Set Up

This paper discusses the damage identification using artificial neural network methods for the benchmark problem set up by IASC-ASCE Task Group on Health Monitoring. A three-stage damage identification strategy for building structures is proposed. The BP network and PNN are employed for damage localization and BP network for damage extent identification. Four damage patterns (patterns i~iv) in Cases 1-6 are discussed. The comparison between BP network and PNN are carried out. The results show that PNN performs better than BP network in damage localization. The damage extent identification using BPN is successful even in Cases 2 and 5&6 in which the modeling error is quite large.

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An Automatic Logistics System using Artificial Intelligence

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1183.0782s419 ◽

2019 ◽

Vol 8 (2S4) ◽

pp. 924-926

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Artificial Neural Network ◽

Optimal Route ◽

Logistics System ◽

The Neural Network ◽

Artificial Intelligence Technology ◽

Artificial Neural ◽

Set Up

In this paper, we propose a method to utilize machine learning to automate the system of classifying and transporting large quantities of logistics. First, establish an environment similar to the task of transferring logistics to the desired destination, and set up basic rules for classification and transfer. Next, each of the logistics that need sorting and transportation is defined as one entity, and artificial intelligence is introduced so that each individual can go to an optimal route without collision between the objects to the destination. Artificial intelligence technology uses artificial neural networks and uses genetic algorithms to learn neural networks. The artificial neural network is generated by each chromosome, and it is evolved based on the most suitable artificial neural network, and a score is given to each operation to evaluate the fitness of the neural network. In conclusion, the validity of this algorithm is evaluated through the simulation of the implemented system.

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Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artificial Neural Network

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2213111607666191111095329 ◽

2020 ◽

Vol 13 (7) ◽

pp. 980-992 ◽

Cited By ~ 1

Author(s):

Muhammad F. Tahir ◽

Chen Haoyong ◽

Kashif Mehmood ◽

Noman A. Larik ◽

Asad Khan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Load Forecasting ◽

Vital Role ◽

Short Term ◽

Training Time ◽

Generalization Ability ◽

Short Term Load Forecasting ◽

Artificial Neural ◽

Bagging Method

Background: Short Term Load Forecasting (STLF) can predict load from several minutes to week plays a vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. Methods: This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, make this algorithm unable to accurately predict future loads. Results: This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which help in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. Conclusion: This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperforms the ANN and Bagging method by further increasing the generalization ability and STLF accuracy.

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Use of an Artificial Neural Network to Assess the Degree of Training of an Operator of Selected Devices Used in Precision Agriculture

Energies ◽

10.3390/en13236329 ◽

2020 ◽

Vol 13 (23) ◽

pp. 6329

Author(s):

Karolina Trzyniec ◽

Adam Kowalewski

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Precision Agriculture ◽

Numerical Data ◽

Neural Model ◽

Operator Training ◽

Training Time ◽

Artificial Neural ◽

The Moment ◽

Parallel Strips

The article concerns the issue of automatic recognition of the moment of achieving the desired degree of training of an operator of devices used in precision agriculture. The aim of the research was to build a neural model that recognizes when an operator has acquired the skill of operating modern navigation on parallel strips used in precision agriculture. To conduct the test, a standard device to assist the operator in guiding the machine along given paths, eliminating overlaps, was selected. The thesis was proven that the moment of operator training (meaning driving along designated paths with an accuracy of up to eight centimeters) can be automatically recognized by a properly selected artificial neural network. This network was learned on the basis of data collected during the observation of the operator training process, using a criterion defined by experts. The data collected in the form of photos of the actual and designated route was converted into numerical data and entered into the network input. The output shows the binary evaluation of the trip. It has been shown that the developed neural model will allow the determining of the moment when operators acquire the skills to drive a vehicle along the indicated path and thus shorten the training time.

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A hybrid artificial neural network - genetic algorithm for load shedding

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i3.pp2250-2258 ◽

2020 ◽

Vol 10 (3) ◽

pp. 2250 ◽

Cited By ~ 1

Author(s):

Le Trong Nghia ◽

Quyen Huy Anh ◽

Phung Trieu Tan ◽

N Thai An

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Back Propagation ◽

Load Shedding ◽

Software Systems ◽

Training Time ◽

Power Load ◽

Artificial Neural ◽

Input Variables

This paper proposes the method of applying Artificial Neural Network (ANN) with Back Propagation (BP) algorithm in combination or hybrid with Genetic Algorithm (GA) to propose load shedding strategies in the power system. The Genetic Algorithm is used to support the training of Back Propagation Neural Networks (BPNN) to improve regression ability, minimize errors and reduce the training time. Besides, the Relief algorithm is used to reduce the number of input variables of the neural network. The minimum load shedding with consideration of the primary and secondary control is calculated to restore the frequency of the electrical system. The distribution of power load shedding at each load bus of the system based on the phase electrical distance between the outage generator and the load buses. The simulation results have been verified through using MATLAB and PowerWorld software systems. The results show that the Hybrid Gen-Bayesian algorithm (GA-Trainbr) has a remarkable superiority in accuracy as well as training time. The effectiveness of the proposed method is tested on the IEEE 37 bus 9 generators standard system diagram showing the effectiveness of the proposed method.

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Prediction of Biosorption Capacity Using Artificial Neural Network Modeling and Genetic Algorithm

Handbook of Research on Manufacturing Process Modeling and Optimization Strategies - Advances in Logistics, Operations, and Management Science ◽

10.4018/978-1-5225-2440-3.ch013 ◽

2017 ◽

pp. 276-290 ◽

Cited By ~ 1

Author(s):

Prakash Chandra Mishra ◽

Anil Kumar Giri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Transfer Function ◽

Bacillus Cereus ◽

Living Cells ◽

Biosorption Capacity ◽

Chromium Vi ◽

Artificial Neural ◽

Hidden Layer ◽

Set Up

Artificial neural network model is applied for the prediction of the biosorption capacity of living cells of Bacillus cereus for the removal of chromium (VI) ions from aqueous solution. The maximum biosorption capacity of living cells of Bacillus cereus for chromium (VI) was found to be 89.24% at pH 7.5, equilibrium time of 60 min, biomass dosage of 6 g/L, and temperature of 30 ± 2 °C. The biosorption data of chromium (VI) ions collected from laboratory scale experimental set up is used to train a back propagation (BP) learning algorithm having 4-7-1 architecture. The model uses tangent sigmoid transfer function at input to hidden layer whereas a linear transfer function is used at output layer. The data is divided into training (75%) and testing (25%) sets. Comparison between the model results and experimental data gives a high degree of correlation R2 = 0.984 indicating that the model is able to predict the sorption efficiency with reasonable accuracy. Bacillus cereus biomass is characterized using AFM and FTIR.

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