Data-driven soft sensor approach for online quality prediction using state dependent parameter models

2017 ◽  
Vol 162 ◽  
pp. 130-141 ◽  
Author(s):  
Bahareh Bidar ◽  
Jafar Sadeghi ◽  
Farhad Shahraki ◽  
Mir Mohammad Khalilipour
Keyword(s):  
Soft Sensor ◽  
Data Driven ◽  
Quality Prediction ◽  
State Dependent ◽  
Dependent Parameter

Data-Driven Soft Sensor Model Based on Deep Learning for Quality Prediction of Industrial Processes

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Xianglin Zhu ◽  
Khalil Ur Rehman ◽  
Wang Bo ◽  
Muhammad Shahzad ◽  
Ahmad Hassan
Keyword(s):  
Deep Learning ◽  
Soft Sensor ◽  
Data Driven ◽  
Industrial Processes ◽  
Quality Prediction ◽  
Model Based ◽  
Sensor Model

Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Nurali Virani ◽  
Devesh K. Jha ◽  
Zhenyuan Yuan ◽  
Ishana Shekhawat ◽  
Asok Ray
Keyword(s):  
Particle Filtering ◽  
Dynamic Models ◽  
Training Data ◽  
Data Driven ◽  
Conditional Density ◽  
Bayesian Filtering ◽  
State Dependent ◽  
State Evolution ◽  
And Control ◽  
Nonparametric Kernel

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

scholarly journals BR-Sensor: An On-line Data-driven Soft Sensor of Downhole Pressure∗∗Final support from Petrobas S.A. is acknowledged.

2015 ◽  
Vol 48 (6) ◽  
pp. 311-316 ◽  
Author(s):  
Edson F.A. Rezende ◽  
Alex F. Teixeira ◽  
Eduardo M.A.M. Mendes
Keyword(s):  
Soft Sensor ◽  
Data Driven ◽  
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