Bearing Fault Detection Through Machine Learning: Time-Domain vs Time-Frequency Analysis for Feature Extraction
F. Ulloa and G. Barbieri
Event name: First EAGE Workshop on High Performance Computing for Upstream in Latin America
Publication date: 21 September 2018
Info: Extended abstract, PDF ( 918.3Kb )
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Machine learning methods have been used for fault detection in condition-based maintenance through the application of different approaches for feature extraction. Feature extraction has a significant influence on the selection and performance of the machine learning algorithm and consequently on the obtained results of the analysis. Within this work, time-domain and time-frequency approaches for feature extraction are compared. The binary classification of the state of a bearing is used as case study for the comparison; i.e. nominal / failure state. In time-domain analysis, time descriptive statistics of the signal are extracted, and a neural network is used for the fault classification. Whereas, in time-frequency analysis, a 1D signal in time is transformed into a 2D image through the utilization of the Short Time Fourier Transform. Then, a convolutional neural network is applied for the fault classification. The time-frequency approach showed better results on the fault classification of the selected application with lower computational costs. Further studies should be performed in order to validate the result.