Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

An Intelligent Machine Monitoring System for Energy Prediction Using a Gaussian Process Regression

Published

Author(s)

Sudarsan Rachuri, Moneer M. Helu

Abstract

Recent advances in machine automation and sensing technology offer new opportunities for continuous condition monitoring of an operating machine. This paper describes an intelligent machine monitoring framework that integrates and utilizes data collection, management, and analytics to derive an adaptive predictive model for the energy usage of a milling machine. This model is designed using a Gaussian Process (GP) regression algorithm, which is a flexible regression method that also provides an uncertainty estimate. To improve computational efficiency, we propose a Collective Gaussian Process (CGP) in which the overall energy prediction is made by constructing local GP models weighted by probability distribution functions obtained using the Gaussian Mixture Model (GMM) technique. Finally, we demonstrate the ability of the proposed monitoring framework to construct an energy prediction model to predict the energy used to machine a part.
Conference Dates
October 27-30, 2014
Conference Location
Washington, DC
Conference Title
IEEE International Conference on BigData 2014

Keywords

Milling tool, Energy prediction, Gaussian Process regression, Data-driven manufacturing, Standards, MTConnect

Citation

Rachuri, S. and Helu, M. (2014), An Intelligent Machine Monitoring System for Energy Prediction Using a Gaussian Process Regression, IEEE International Conference on BigData 2014, Washington, DC, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=916364 (Accessed April 20, 2024)
Created October 27, 2014, Updated February 19, 2017