Flow Regime Identification Using Neural Network–Based Electrodynamic Tomography System

2012 ◽  
Author(s):  
Mohd. Fua’ad Rahmat ◽  
Hakilo Ahmed Sabit
Keyword(s):  
Neural Network ◽  
Flow Regime ◽  
Low Cost ◽  
Industrial Process ◽  
Solid Particles ◽  
Non Invasive ◽  
Tomography System ◽  
Process Tomography ◽  
Flow Regime Identification ◽  
Cost Efficient

Proses tomografi adalah suatu teknik membina imej yang murah, cekap dan sesuai untuk proses di industri yang kini semakin diguna pakai untuk tujuan pemantauan proses dan pengukuran. Mekanisme pengesanan dalam proses tomografi bergantung kepada bahan aliran dalam paip industri sama ada pepejal, gas atau cecair. Dalam kertas kerja ini, proses yang terlibat adalah pengaliran pepejal kering dalam paip mengikut arah graviti dan mekanisme pengesanan yang digunakan ialah penderia elektrodinamik. Pengenalpastian rejim aliran daripada pengukuran penderia adalah dengan menggunakan rangkaian neural yang akan mengenal pasti aliran pepejal sama ada dalam aliran penuh, suku separuh dan tiga suku. Kata kunci: Proses tomografi, rangkaian neural, penderia elektrodinamik, pengenalpastian Process tomography is a low cost, efficient and non-invasive industrial process imaging technique. It is used in many industries for process imaging and measuring. Provided that appropriate sensing mechanism is used, process tomography can be used in processes involving solids, liquids, gases, and any of their mixtures. In this paper, the process to be imaged and measured involves solid particles flow in gravity drop system. Electrical charge tomography or electrodynamic tomography is a tomographic technique using electrodynamic sensors. This paper presents the flow regime identification using neural network. Keywork: Process tomography; neural network; electrodynamic sensor; identification

Application Of Neural Network Technique And Electrodynamic Sensors In The Identification Of Solid Flow Regimes

2012 ◽  
Author(s):  
Mohd Fua’ad Hj Rahmat ◽  
Hakilo Ahmed Sabit
Keyword(s):  
Neural Network ◽  
Concentration Profile ◽  
Prediction Models ◽  
Flow Regimes ◽  
Industrial Processes ◽  
Concentration Profiles ◽  
Tomography System ◽  
Process Tomography ◽  
Flow Regime Identification ◽  
Profile Flow

Imaging of industrial processes have been accomplished with better efficiency and better control since the introduction of process tomography in several industries. This technique enables a deeper look into the internal conditions of a process without invading the process. In tomographic techniques, process information such as the distribution and velocity of the particles conveying at a particular plane can be obtained by placing sensors around the periphery of the plane. This paper is a continuation of a previous paper entitled Flow Regime Identification Using Neural Network–based Electrodynamic Tomography System in Jurnal Teknologi 40(D). This paper presents the results of sensors output in comparison to that of prediction models, concentration profiles and flow regimes identification obtained from the system described in the previous paper. Key words: Electrodynamic tomography, neural network, concentration profile, flow regimes

Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

2022 ◽  
Author(s):  
Massine GANA ◽  
Hakim ACHOUR ◽  
Kamel BELAID ◽  
Zakia CHELLI ◽  
Mourad LAGHROUCHE ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Vibration Analysis ◽  
Low Cost ◽  
Piezoelectric Sensor ◽  
Integrated System ◽  
Easy Access ◽  
Non Invasive ◽  
Artificial Neural

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

Intelligent Identification of Flow Regime Based on a Novel Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 1715-1718
Author(s):  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Flow Regime ◽  
Recurrent Neural Network ◽  
Pressure Difference ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Elman Neural Network ◽  
Flow Regime Identification ◽  
Novel Method

A noveol neural network of Elman is typically dynamic recurrent neural network. A novel method of flow regime identification based on Elman neural network and wavelet packet decomposition is proposed in this paper. Above all, the collected pressure-difference fluctuation signals are decomposed by the four-layer wavelet packet, and the decomposed signals in various frequency bands are obtained within the frequency domain. Then the wavelet packet energy eigenvectors of flow regimes are established. At last the wavelet packet energy eigenvectors are input into Elman neural network and flow regime intelligent identification can be performed.

scholarly journals Development a low-cost electrical impedance tomography system for non-invasively detecting abnormal in breast tissue - Phantom study-

10.29007/x6vj ◽  
2022 ◽  
Author(s):  
Minh Quan Cao Dinh ◽  
Quoc Tuan Nguyen Diep ◽  
Hoang Nhut Huynh ◽  
Ngoc An Dang Nguyen ◽  
Anh Tu Tran ◽  
...  
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Low Cost ◽  
Impedance Tomography ◽  
Non Invasive ◽  
Tomography System ◽  
Tissue Phantom ◽  
Cost System ◽  
Invasive Method ◽  
Breast Tissues

Electrical Impedance Tomography (EIT) is known as non-invasive method to detect and classify the abnormal breast tissues. Reimaging conductivity distribution within an area of the subject reveal abnormal tissues inside that area. In this work, we have created a very low-cost system with a simple 16-electrode phantom for doing research purposes. The EIT data were measured and reconstructed with EIDORS software.

Neural network based objective flow regime identification in air-water two phase flow

1994 ◽  
Vol 72 (3) ◽  
pp. 440-445 ◽  
Author(s):  
Shiqian Cai ◽  
Haluk Toral ◽  
Jianhung Qiu ◽  
John S. Archer
Keyword(s):  
Neural Network ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Regime Identification

FPGA TECHNOLOGY IN PROCESS TOMOGRAPHY

2016 ◽  
Vol 78 (7-4) ◽  
Author(s):  
Lean Thiam Siow ◽  
Mohd Hafiz Fazalul Rahiman ◽  
Ruzairi Abdul Rahim ◽  
Mohd Shukry Abdul Majid ◽  
Salman Sayyidi Hamzah ◽  
...  
Keyword(s):  
High Speed ◽  
Low Cost ◽  
Gate Arrays ◽  
Tomography System ◽  
Process Tomography ◽  
Ultrasonic Process ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Future Work

The aims of this paper are to provide a review of the process tomography applications employing field programmable gate arrays (FPGA) and to understand current FPGA related researches, in order to seek for the possibility to applied FPGA technology in an ultrasonic process tomography system. FPGA allows users to implement complete systems on a programmable chip, meanwhile, five main benefits of applying the FPGA technology are performance, time to market, cost, reliability, and long-term maintenance. These advantages definitely could help in the revolution of process tomography, especially for ultrasonic process tomography and electrical process tomography. Future work is focused on the ultrasonic process tomography for chemical process column investigation using FPGA for the aspects of low cost, high speed and reconstructed image quality.

Two-phase Flow Regime Identification Combining Conductivity Probe Signals and Artificial Neural Network

2007 ◽  
Author(s):  
Leonor Hernández ◽  
José Enrique Juliá ◽  
Sergio Chiva ◽  
Sidharth Paranjape ◽  
Mamoru Ishii
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Conductivity Probe ◽  
Flow Regime Identification ◽  
Artificial Neural
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