Open AccessA Fault Diagnosis Model Based On Full Vector Spectrum And Feature Engineering
Author(s) -
Huanting Cui,
Hong Chen,
Sa Xiao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1605/1/012017
Subject(s) - feature (linguistics) , rotor (electric) , fault (geology) , feature vector , spectrum (functional analysis) , process (computing) , computer science , feature engineering , engineering , pattern recognition (psychology) , data mining , artificial intelligence , algorithm , seismology , deep learning , geology , mechanical engineering , physics , philosophy , linguistics , quantum mechanics , operating system
Aiming at the problem of how to quickly and accurately diagnose different types of rotating machinery faults, this paper starts with the combination of full-vector spectrum theory and feature engineering. A model of rotor imbalance faults diagnosis and identifying is constructed in the process of dealing with the data sets of multiple rotor imbalance fault. The result shows that this fault diagnosis model combined with the full vector spectrum theory and feature engineering can be used to correctly distinguish the faults of the equipment and accurately identify the types of the unbalanced faults.