scholarly journals Failure Prediction of Induction Motors: A Case Study using CSLGH900/6-214, 5.8 MW, 11 kV/3ph/50 Hz Sag Mill Motor at Goldfields, Damang Mine

2020 ◽  
pp. 7-28
Author(s):  
C. K. Amuzuvi ◽  
H. Warden
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intelligent Systems ◽  
Induction Motors ◽  
Failure Prediction ◽  
Fault Prediction ◽  
Feed Forward ◽  
Sag Mill ◽  
Three Phase ◽  
Artificial Neural

This paper proposes a generalised feed-forward artificial neural network model that fulfils the failure prediction of a three phase 5.8MW, 11 kV Slip-Ring SAG Mill Induction Motor at Goldfields Ghana Limited, Damang Mine. It provides a general understanding of three phase induction motors,faults associated with induction motors and also emphasizes the use of intelligent systems, particularly artificial neural network, a modern failure prediction technology of induction motors. Site analysis and motor data (Current, Power and Winding Temperatures) collection were conducted at the Damang Mine. Simulation results are presented using MATLAB software (2017a) package to develop the fault prediction model. The proposed feed-forward neural network used the Levenberg-Marquardt and Bayesian Regularisation in training.

scholarly journals Kontrol Kecepatan Motor Induksi menggunakan Algoritma Backpropagation Neural Network

2018 ◽  
Vol 5 (2) ◽  
pp. 138
Author(s):  
MUHAMMAD RUSWANDI DJALAL ◽  
KOKO HUTORO ◽  
ANDI IMRAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Induction Motors ◽  
Network Control ◽  
Neural Network Control ◽  
Backpropagation Neural Network ◽  
Three Phase ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRAKBanyak strategi kontrol berbasis kecerdasan buatan telah diusulkan dalam penelitian seperti Fuzzy Logic dan Artificial Neural Network (ANN). Tujuan dari penelitian ini adalah untuk mendesain sebuah kontrol agar kecepatan motor induksi dapat diatur sesuai kebutuhan serta membandingkan kinerja motor induksi tanpa kontrol dan dengan kontrol. Dalam penelitian ini diusulkan sebuah metode artificial neural network untuk mengontrol kecepatan motor induksi tiga fasa. Kecepatan referensi motor diatur pada kecepatan 140 rad/s, 150 rad/s, dan 130 rad/s. Perubahan kecepatan diatur pada setiap interval 0.3 detik dan waktu simulasi maksimum adalah 0,9 detik. Kasus 1 tanpa kontrol, menunjukkan respon torka dan kecepatan dari motor induksi tiga fasa tanpa kontrol. Meskipun kecepatan motor induksi tiga fasa diatur berubah pada setiap 0,3 detik tidak akan mempengaruhi torka. Selain itu, motor induksi tiga fasa tanpa kontrol memiliki kinerja yang buruk dikarenakan kecepatan motor induksi tidak dapat diatur sesuai dengan kebutuhan. Kasus 2 dengan control backpropagation neural network, meskipun kecepatan motor induksi tiga fasa berubah pada setiap 0.3 detik tidak akan mempengaruhi torsi. Selain itu, kontrol backpropagation neural network memiliki kinerja yang baik dikarenakan kecepatan motor induksi dapat diatur sesuai dengan kebutuhan.Kata kunci: Backpropagation Neural Network (BPNN), NN Training, NN Testing, Motor.ABSTRACTMany artificial intelligence-based control strategies have been proposed in research such as Fuzzy Logic and Artificial Neural Network (ANN). The purpose of this research was design a control for the induction motor speed that could be adjusted as needed and compare the performance of induction motor without control and with control. In this research, it was proposed an artificial neural network method to control the speed of three-phase induction motors. The reference speed of motor was set at the rate of 140 rad / s, 150 rad / s, and 130 rad / s. The speed change was set at every 0.3 second interval and the maximum simulation time was 0.9 seconds. Case 1, without control, shows the torque response and velocity of three-phase induction motor without control. Although the speed of three phase induction motor was set to change at every 0.3 seconds, it would not affect the torque. The uncontrolled three-phase induction motors had poor performance due to induction motor speeds could not be adjusted as needed. Case 2 with backpropagation neural network control, although the speed of three phase induction motor changing at every 0.3 seconds would not affect the torque. In addition, the backpropagation neural network control had a good performance because the speed of induction motor could be adjusted as needed.Keywords: Backpropagation Neural Network (BPNN), NN Training, NN Testing, Motor

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

Feed-forward artificial neural network provides data-driven inference of functional connectivity

2019 ◽  
Vol 29 (9) ◽  
pp. 091101 ◽  
Author(s):  
Nikita Frolov ◽  
Vladimir Maksimenko ◽  
Annika Lüttjohann ◽  
Alexey Koronovskii ◽  
Alexander Hramov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Functional Connectivity ◽  
Data Driven ◽  
Feed Forward ◽  
Artificial Neural

scholarly journals Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yasir Hassan Ali ◽  
Roslan Abd Rahman ◽  
Raja Ishak Raja Hamzah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Film Thickness ◽  
Spur Gear ◽  
Back Propagation Algorithm ◽  
Elman Neural Network ◽  
Feed Forward ◽  
Oil Film ◽  
Feed Forward Network ◽  
Artificial Neural

The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN) computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (λ). The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.

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