Open AccessBearing Fault Detection Using DWT and CNN
Author(s) -
Shubham D. Paranjape,
Jitendra A. Gaikwad
Publication year - 2021
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/cseit2174116
Subject(s) - bearing (navigation) , fault (geology) , vibration , moment of inertia , moment (physics) , computer science , actuator , fault detection and isolation , main bearing , artificial intelligence , engineering , mechanical engineering , acoustics , geology , crankshaft , physics , classical mechanics , quantum mechanics , seismology
Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. There are various methods introduced for condition monitoring such as vibration analysis, temperature analysis, wear and debris analysis, image processing etc. Among this image analysis is found to be the most effective method for detection of machine faults. This paper proposes an automatic fault diagnosis method for bearings based on a DWT and CNN.