Session: VIB-08-01 Vibration and Stability of Mechanical Systems and Machine Learning Applications to Vibrations and Dynamics

Paper Number: 66701

Start Time: August 19, 03:20 PM

66701 - Data Augmentation for Roller Bearing Health Indicator Estimation Using Multi-Channel Frequency Data Representations 

This paper demonstrates various data augmentation techniques that can be used when working with limited run-to-failure data to estimate health indicators related to the remaining useful life of roller bearings. The PRONOSTIA bearing prognosis dataset is used for benchmarking data augmentation techniques. The input to the networks are multi-dimensional frequency representations obtained by combining the spectra taken from two accelerometers. Data augmentation techniques are adapted from other machine learning fields and include adding Gaussian noise, region masking, masking noise, and pitch shifting. Augmented datasets are used in training a conventional CNN architecture comprising two convolutional and pooling layer sequences with batch normalization. Results from individually separating each bearing’s data for the purpose of validation shows that all methods, except pitch shifting, give improved validation accuracy on average. Masking noise and region masking both show the added benefit of dataset regularization by giving results that are more consistent after repeatedly training each configuration with new randomly generated augmented datasets. It is shown that gradually deteriorating bearings and bearings with abrupt failure are not treated significantly differently by the augmentation techniques.

 

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Presenting Author: Jacob Hendriks University of Ottawa

Authors:

Jacob Hendriks University of Ottawa
Patrick Dumond University of Ottawa