Rotating machinery fault diagnosis using a quadratic neural unit. (14th July 2022)

• Record Type: Journal Article
• Authors: Rodríguez-Jorge, Ricardo; Sánchez-Pérez, Laura; Bila, Jiri; Škvor, Jiří
• Is Part Of: International journal of grid and utility computing. Volume 13:Number 2/3(2022)
• Page Start: 309
• Page End: 319
• Publication Date: 2022-07-14
• Subjects: quadratic neural unit -- industrial machine -- predictive maintenance -- vibration test -- alternating current motor; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

Abstract

In this work, a quadratic neural unit was implemented for rotating machinery fault diagnoses of an industrial machine, where the input data that were used were taken from a vibration test on an alternating current motor. The data that were obtained from the vibrometre were the frequency and the average of the vibration, which were previously trained and input into the neural unit. The output of this unit was a value that can be used to categorize the severity level of an engine, according to the severity table provided by the norm ISO 10816 for industrial machines.