@article{887,
  author = {Brahim Harhoud, Messaoud Ramdani},
  title = {Automatic Bearing Fault Diagnosis Using Vibration Signal Analysis and Fuzzy Logic},
  journal = {Journal of Electronic Systems},
  year = {2012},
  volume = {2},
  number = {2},
  doi = {},
  url = {http://www.dline.info/jes/fulltext/v2n2/3.pdf},
  abstract = {Condition monitoring systems using vibration measurements and supervised classifiers can be used to automate the diagnosis process of rotating machines. In this paper, we describe an automatic diagnosis system for detection and classification of defects in ball bearings using a time varying parametric spectrum estimation method for analyzing nonstationary vibration signals. The classification task is accomplished by an adaptive neural fuzzy inference system. The designed system was developed to be able to classify four types of preestablished defects in ball bearings operating under several shaft speeds and load conditions. The system was tested with experimental data collected from drive end ball bearing of an induction motor driven by mechanical system.},
}