<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, implementation of soft sensing technique for measurement of fluid flow rate is reported. The objective of the paper is to design an estimator to physically measure the flow in pipe by analysing the vibration on the walls of the pipe. Commonly used head type flow meter causes obstruction to the flow and measurement would depend on the placement of these sensors. In the proposed technique vibration sensor is bonded on the pipe of liquid flow. It is observed that vibration in the pipe varies with the control action of stem. Single axis accelerometer is used to acquire vibration signal from pipe, signal is passed from the sensor to the system for processing. Basic techniques like filtering, amplification, and Fourier transform are used to process the signal. The obtained transform is trained using neural network algorithm to estimate the fluid flow rate. Artificial neural network is designed using back propagation with artificial bee colony algorithm. Designed estimator after being incorporated in practical setup is subjected to test and the result obtained shows successful estimation of flow rate with the root mean square percentage error of 0.667.</p></abstract>

In this paper, implementation of soft sensing technique for measurement of fluid flow rate is reported. The objective of the paper is to design an estimator to physically measure the flow in pipe by analysing the vibration on the walls of the pipe. Commonly used head type flow meter causes obstruction to the flow and measurement would depend on the placement of these sensors. In the proposed technique vibration sensor is bonded on the pipe of liquid flow. It is observed that vibration in the pipe varies with the control action of stem. Single axis accelerometer is used to acquire vibration signal from pipe, signal is passed from the sensor to the system for processing. Basic techniques like filtering, amplification, and Fourier transform are used to process the signal. The obtained transform is trained using neural network algorithm to estimate the fluid flow rate. Artificial neural network is designed using back propagation with artificial bee colony algorithm. Designed estimator after being incorporated in practical setup is subjected to test and the result obtained shows successful estimation of flow rate with the root mean square percentage error of 0.667.

Estimation of Flow Rate Through Analysis of Pipe Vibration

Department of Instrumentation and Control Engineering, Manipal Institute of Technology

Acta Mechanica et Automatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Estimation of Flow Rate Through Analysis of Pipe Vibration"}

In this paper, implementation of soft sensing technique for measurement of fluid flow rate is reported. The objective of the paper is to design an estimator to...

Estimation of Flow Rate Through Analysis of Pipe Vibration

Data publikacji: 03 sty 2019

Zakres stron: 294 - 300

Otrzymano: 09 wrz 2018

Przyjęty: 19 gru 2018

DOI: https://doi.org/10.2478/ama-2018-0045

Słowa kluczowe
Accelerometer, Estimation, Frequency Response, Flow Rate, Neural Network, Vibration

© 2018 Santhosh K. Venkata et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Estimation of Flow Rate Through Analysis of Pipe Vibration

Data publikacji: 03 sty 2019

Zakres stron: 294 - 300

Otrzymano: 09 wrz 2018

Przyjęty: 19 gru 2018

DOI: https://doi.org/10.2478/ama-2018-0045

Słowa kluczoweAccelerometer, Estimation, Frequency Response, Flow Rate, Neural Network, Vibration

© 2018 Santhosh K. Venkata et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
Accelerometer, Estimation, Frequency Response, Flow Rate, Neural Network, Vibration