Identification of Diagenetic Facies in Low-Permeability Sandstone Reservoirs Based on Self-Organizing-Map Neural Network Algorithm

2020 ◽  
Author(s):  
Yan Lu ◽  
Keyu Liu ◽  
Ya Wang
Keyword(s):  
Neural Network ◽  
Low Permeability ◽  
Self Organizing Map ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Sandstone Reservoirs ◽  
Diagenetic Facies ◽  
Self Organizing

A New Approach to Machinery Monitoring and Diagnostics Using Self-Organizing Maps

1995 ◽  
Author(s):  
Siyu Zhang ◽  
R. Ganesan ◽  
T. S. Sankar
Keyword(s):  
Neural Network ◽  
Service Life ◽  
Trend Analysis ◽  
New Approach ◽  
Machine System ◽  
Network Algorithm ◽  
On Line ◽  
Neural Network Algorithm ◽  
Monitoring And Diagnostics ◽  
Self Organizing

Abstract The problem of estimating an unknown multivariate function from on-line vibration measurements, for determining the conditions of a machine system and for estimating its service life is considered. This problem is formulated into a multiple-index based trend analysis problem and the corresponding indices for trend analysis are extracted from the on-line vibration data. Selection of these indices is based on the simultaneous consideration of commonly-observed faults or malfunctions in the machine system being monitored. A neural network algorithm that has been developed by the present authors for multiple-index based regression is adapted to perform the trend analysis of a machine system. Applications of this neural network algorithm to the condition monitoring and life estimation of both a bearing system as well as a gearbox are fully demonstrated. The efficiency and computational supremacy of the new algorithm are established through comparing with the performance of Self-Organizing Mapping (SOM) and Constrained Topological Mapping (CTM) algorithms. Further, the usefulness of multiple-index based trend analysis in precisely predicting the condition and service life of a machine system is clearly demonstrated. Using on-line vibration signal to constitute the set of variables for trend analysis, and employing the newly-developed self-organizing neural algorithm for performing the trend analysis, a new approach is developed for machinery monitoring and diagnostics.

scholarly journals IMPLEMENTASI METODE KOHONEN UNTUK PREDIKSI CURAH HUJAN (STUDI KASUS : KOTA PONTIANAK)

2017 ◽  
Vol 4 (2) ◽  
pp. 198
Author(s):  
Fatma Agus Setyanngsih
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Self Organizing Map ◽  
Rainfall Prediction ◽  
The Neural Network ◽  
Network Time ◽  
Neural Network Algorithm ◽  
Network Prediction ◽  
The City ◽  
Self Organizing

<p><em>The prediction to determine the rainfall in Pontianak is much needed. One of them is using a neural network algorithm using SOM (Self Organizing Maping) with the data used in January 2010-2013. The purpose of this study was to determine the rainfall prediction in the city of Pontianak with parameters of air temperature, relative humidity, air pressure and wind speed. The results showed that the value of MSE is obtained when studying the data network prediction in January of 2010 until 2013 using the Neural Network-SOM learning process with the amount of 1 neuron and using 124 datas, with MSE value 0,0148.</em><strong> </strong></p><p><strong><em>Keywords</em></strong><em>: </em><em>Rainfall, Neural Network, Time Series, Self Organizing Map</em></p><p><em>Prediksi untuk mengetahui curah hujan yang terjadi di Pontianak sangat dibutuhkan salah satunya yaitu menggunakan algoritma jaringan syaraf tiruan dengan pengelompokkannya menggunakan SOM (Self Organizing Map) dengan data yang digunakan adalah data di bulan januari tahun 2010-2013. Tujuan dari penelitian ini adalah untuk mengetahui prediksi curah hujan di kota Pontianak dengan parameter suhu udara, kelembababn relative, tekanan udara dan kecepatan angin. Hasil penelitian menunjukkan bahwa nilai MSE ini didapatkan saat jaringan mempelajari data prediksi pada bulan januari di tahun 2010 sampai tahun 2013 dengan menggunakan proses pembelajaran JST SOM dengan jumlah neuron 1 dan menggunakan 124 data, dengan nilai MSE 0,0148. </em></p><p><em></em><em><strong><em>Kata kunci</em></strong><strong><em>:</em></strong><em> </em><em>Curah Hujan, Jaringan Syaraf Tiruan, Time Series, Self Organizing Map</em></em></p>

scholarly journals Identification of Motion Conditions Based on Self-organizing Competitive Neural Network Algorithm

2018 ◽  
Vol 439 ◽  
pp. 032092
Author(s):  
Xu Guolei ◽  
Zhou Nianrong ◽  
Tang Lijun ◽  
Zhang Wenbin
Keyword(s):  
Neural Network ◽  
Competitive Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Self Organizing

Self-Organising Features of Rotor-Dynamic Faults

2007 ◽  
Author(s):  
Nalinaksh S. Vyas ◽  
Ankur Kumar
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Self Organizing Map ◽  
Dynamic Faults ◽  
Monitoring Strategies ◽  
Orderly Fashion ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Low Dimensional ◽  
Self Organizing

Artificial Neural Networks offer an efficient platform for devising condition monitoring strategies in machinery and plants where the number of components and processes are too many and complex to be mathematically modeled appropriately. Self-Organizing Map (SOM) is an interesting artificial neural network algorithm which produces an ordered low dimensional representation of an input data space. The basic Self-Organizing Map (SOM) can be visualized as a sheet-like neural-network array, the cells (or nodes) of which become specifically tuned to various input signal patterns or classes of patterns in an orderly fashion. This work describes a study on application of such topographic mapping for rotordynamic faults. A variety of rotor faults like unbalance, bearing damage, cocked rotor etc., are simulated on an experimental rig in a controlled manner. Vibration Signals obtained from various sensors on the rig are processed to train a Self-Organising Neural Network. It is has been shown that input neurons from the same fault get mapped together in clusters in both two-dimensional and three-dimensional grid spaces and each individual fault occupies a distinct zone on the grid.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm
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