Title :
Neural network based data validation algorithm for pressure processes
Author :
Mounika, Batthina ; Raghu, Guntupalli ; Sreelekha, Surabhi ; Jeyanthi, R.
Author_Institution :
Dept. ECE, Amrita Vishwa Vidyapeetham, Bangalore, India
Abstract :
Effective Control of Complex process in power generation industries is a challenge to instrumentation engineers. Most importantly coordination of sensor functioning should be monitored regularly. So, a sensor validation and data reconciliation methods are adopted for monitoring the data and faulty data are replaced. In this paper neural network based data validation method is developed and tested for pressure sensor. For neural networks, back propagation algorithm is applied for obtaining an estimated value and then a fault detection method called SPRT (Sequential probability ratio test) is applied to identify the trueness of the sensor.
Keywords :
backpropagation; fault diagnosis; neurocontrollers; power generation control; pressure sensors; process control; sequential estimation; SPRT; back propagation algorithm; data reconciliation method; data validation algorithm; fault detection method; neural network; power generation industries; pressure sensor; process control; sensor validation method; sequential probability ratio test; Artificial neural networks; Biological neural networks; Instruments; Neurons; Process control; Training; Artificial Neural networks (ANN); Data validation; SPRT; back propagation;
Conference_Titel :
Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on
Conference_Location :
Kanyakumari
Print_ISBN :
978-1-4799-4191-9
DOI :
10.1109/ICCICCT.2014.6993147