Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System

07/28/2021
by   Bang Xiang Yong, et al.
0

Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. While accuracy is important, focusing primarily on it poses an overfitting danger, exposing manufacturers to risk, ultimately hindering the adoption of these techniques. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty in a cyber-physical manufacturing system (CPMS) scenario. Then, we propose a multi-agent system architecture which leverages probabilistic machine learning as a means of achieving such criteria. We propose possible scenarios for which our proposed architecture is useful and discuss future work. Experimentally, we implement Bayesian Neural Networks for multi-tasks classification on a public dataset for the real-time condition monitoring of a hydraulic system and demonstrate the usefulness of the system by evaluating the probability of a prediction being accurate given its uncertainty. We deploy these models using our proposed agent-based framework and integrate web visualisation to demonstrate its real-time feasibility.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/09/2020

HACK3D: Evaluating Cybersecurity of Additive Manufacturing by Crowdsourcing

Additive manufacturing cyber-physical system is vulnerable to both cyber...
research
08/17/2020

An Architectural Design for Measurement Uncertainty Evaluation in Cyber-Physical Systems

Several use cases from the areas of manufacturing and process industry, ...
research
10/21/2020

I-nteract 2.0: A Cyber-Physical System to Design 3D Models using Mixed Reality Technologies and Deep Learning for Additive Manufacturing

I-nteract is a cyber-physical system that enables real-time interaction ...
research
12/12/2022

Agnostic Learning for Packing Machine Stoppage Prediction in Smart Factories

The cyber-physical convergence is opening up new business opportunities ...
research
05/31/2021

Transfer Learning as an Enhancement for Reconfiguration Management of Cyber-Physical Production Systems

Reconfiguration demand is increasing due to frequent requirement changes...
research
10/05/2016

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products

Machine learning algorithms increasingly influence our decisions and int...
research
07/16/2021

LeanML: A Design Pattern To Slash Avoidable Wastes in Machine Learning Projects

We introduce the first application of the lean methodology to machine le...

Please sign up or login with your details

Forgot password? Click here to reset