Gearbox Broken Tooth Severity Classification using EMD of Acoustic Emission Signals
- Resource Type
- Conference
- Authors
- Medina, Ruben; Sanchez, Rene-Vinicio; Cabrera, Diego; Ortega, Luis-Renato; Cerrada, Mariela
- Source
- 2022 IEEE Sixth Ecuador Technical Chapters Meeting (ETCM) Technical Chapters Meeting (ETCM), 2022 IEEE Sixth Ecuador. :01-06 Oct, 2022
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Vibrations
Analytical models
Acoustic emission
Feature extraction
Matrices
Linear discriminant analysis
Time-domain analysis
Gearbox
Broken tooth severity
Empirical Mode Decomposition
Random Forest
Linear Discriminant Analysis
- Language
Fault severity classification is a critical task necessary for optimal predictive maintenance to reduce costs and avoid catastrophic accidents in the industry. In this research, we propose a methodology for broken tooth severity classification in a gearbox using digital signal processing techniques of acoustic emission signals. The method uses empirical mode decomposition of the signal and extraction of time-domain features from a set of Intrinsic Mode Functions. The extracted features are fed to random forest and linear discriminant analysis models for attaining the classification of nine different severity conditions. The method provides classification accuracies higher than 90% with both machine learning models.