Open AccessNon‐invasive sound‐based classifier of bearing faults in electric induction motors
Author(s) -
Santos Herman,
Scalassara Paulo,
Endo Wagner,
Goedtel Alessandro,
Guedes Jacqueline,
Gentil Murillo
Publication year - 2021
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/smt2.12044
Subject(s) - induction motor , acoustic emission , engineering , bearing (navigation) , classifier (uml) , computer science , feature extraction , voltage , fault (geology) , sensitivity (control systems) , pattern recognition (psychology) , electronic engineering , artificial intelligence , acoustics , electrical engineering , physics , seismology , geology
Induction motors play a major role in the industry nowadays due to their simple construction, uncomplicated maintenance, and cost efficiency. As the motor operates for repeated hours, some faults may occur and, depending on the process sensitivity, can cause significant losses to the industrial production. In this context, an alternative method is proposed to detect and classify bearing faults using acoustic emission signals generated by the machines and simple features obtained from them. A pair of condenser microphones was used to acquire these signals and audio feature extraction is performed to obtain time and frequency patterns to characterize healthy and faulty machines. The major differences of the acoustic signals regarding the fault frequency signatures are discussed by analyzing specific peaks observed in their spectra. Several coupled load values and levels of power supply voltage unbalance were considered in the experimental tests, which emulate common situations encountered in industrial environments, obtaining accuracy rates over 97% of success. Finally, a comparison is presented of machine learning techniques for bearing faults classification under different load values and voltage unbalance levels.