Gear Crack Level Classification by Using KNN and Time-Domain Features from Acoustic Emission Signals under Different Motor Speeds and Loads

Rene Vinicio Sanchez, Pablo Lucero, Jean Carlo Macancela, Mariela Cerrada, Diego Cabrera, Rafael Vasquez

Research output: Contribution to conferencePaper

Abstract

Diagnosing failures during their initial stage is important to avoid unexpected stops and catastrophic damages, specially for gear boxes that are crucial components in industrial machines. This work addresses the classification of nine levels of crack failure severity in a gearbox. First of all, features are extracted in time domain from signals coming from an acoustic emission (AE) sensor, and then selected by using four different ranking methods. The classification stage uses the k-Nearest Neighbors (KNN) technique. The results indicate that presented levels of severity can be successfully classified with five features extracted from the AE signal for the four ranking methods.

Original languageEnglish
Pages465-470
Number of pages6
DOIs
StatePublished - 11 Mar 2019
EventProceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 -
Duration: 11 Mar 2019 → …

Conference

ConferenceProceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018
Period11/03/19 → …

Keywords

  • Acoustic emission
  • K-nearest neighbors
  • fault diagnosis
  • feature time-domain
  • gear crack

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