首页|Studies from University of Extremadura Update Current Data on Machine Learning ( Industrial Internet of Things Embedded Devices Fault Detection and Classificatio n. a Case Study)
Studies from University of Extremadura Update Current Data on Machine Learning ( Industrial Internet of Things Embedded Devices Fault Detection and Classificatio n. a Case Study)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Caceres,Spain,by New sRx correspondents,research stated,"Industries transition to the Industry 4.0 paradigm requires solutions based on devices attached to machines that allow mon itoring and control of industrial equipment. Monitoring is essential to ensure d evices' proper operation against different aggressions." Financial supporters for this research include State Research Agency,Government of Extremadura (Spain). Our news journalists obtained a quote from the research from the University of E xtremadura,"We propose an approach to detect and classify faults that are typic al in these devices,based on machine learning techniques that use energy,proce ssing,and main application use as features. The proposal was validated using a dataset collected from a testbed executing a typical equipment monitoring applic ation. The proposed machine learning pipeline uses a decision tree-based model f or fault detection (with 99.4% accuracy,99.7% preci sion,99.6% recall,75.2% specificity,and 99.7% F1) followed by a Semi-Supervised Graph-Based model (with 99.3% ac curacy,96.4% precision,96.1% recall,99.6% specificity,and 96.2% F1) for further fault classification."
CaceresSpainEuropeCyborgsEmergin g TechnologiesMachine LearningUniversity of Extremadura
NETL
NSTL
