首页|Investigators at University of Naples Federico Ⅱ Detail Findings in Machine Learning (A Fault Detection Strategy for an Epump During Eol Tests Based On a Knowledge-based Vibroacoustic Tool and Supervised Machine Learning Classifiers)
Investigators at University of Naples Federico Ⅱ Detail Findings in Machine Learning (A Fault Detection Strategy for an Epump During Eol Tests Based On a Knowledge-based Vibroacoustic Tool and Supervised Machine Learning Classifiers)
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Springer Nature
2024 FEB 27 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Machine Learning. According to news reporting originating in Naples, Italy, by NewsRx journalists, research stated, “This paper presents a methodology for identifying faulty components in an electric pump during the end-of-line test based on accelerations and pressure pulsation data used to train an ensemble learning algorithm based on supervised machine learning classifiers. Despite various quality control measures in pump manufacturing, some out-of-tolerance components can pass through and end up on the assembly line, potentially leading to premature failure or abnormal noise during real-field operation.” Financial supporters for this research include Universit degli Studi di Napoli Federico II, Fluid-o-Tech s.rl.
NaplesItalyEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of Naples Federico Ⅱ
NETL
NSTL
Springer Nature
