Exploiting heterogeneous data for the estimation of particles size distribution in industrial plants

Rossetti, Damiano and Squartini, Stefano and Collura, Stefano and Zhang, Yu (2016) Exploiting heterogeneous data for the estimation of particles size distribution in industrial plants. In: 17th International Conference on Mechatronics – Mechatronika 2016, 7-9 December 2016, Prague, Czech Republic.

Documents
paper_pretrain.pdf
[img]
[Download]
[img]
Preview
PDF
paper_pretrain.pdf - Whole Document

407kB
Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive

Abstract

In industrial environments, it is often difficult and expensive to collect a good amount of data to adequately train expert systems for regression purposes. Therefore the usage of already available data, related to environments showing similar characteristics, could represent an effective approach to find a good balance between regression performance and the amount of data to gather for training. In this paper, the authors propose two alternative strategies for improving the regression performance by using heterogeneous data, i.e. data coming from diverse environments with respect to the one taken as reference for testing. These strategies are based on a standard machine learning algorithm, i.e. the Artificial Neural Network (ANN). The employed data came from measurements in industrial plants for energy production through the combustion of coal powder. The powder is transported in air within ducts and its size is detected by means of Acoustic Emissions (AE) produced by the impact of powder on the inner surface of the duct. The estimation of powder size distribution from AE signals is the task addressed in this work. Computer simulations show how the proposed strategies achieve a relevant improvement of regression performance with respect to the standard approach, using ANN directly on the dataset related to the reference plant.

Keywords:Heterogeneous Data, Particles Size Distribution, Acoustic Emissions, Artificial Neural Network
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H713 Production Processes
Divisions:College of Science > School of Engineering
Related URLs:
ID Code:25218
Deposited On:22 Nov 2016 14:53

Repository Staff Only: item control page