Reduced Data Volumes through Hybrid Machine Learning Compared to Conventional Machine Learning Demonstrated on Bearing Fault Classification

Abstract

In some real-world problems, machine learning is faced with little data due to limited resources such as sensors, time, and budget. In this case, the conventional machine learning approach may fail or perform badly. To develop a well-functioning model with a small training set the hybrid machine learning approach, the combination of different methods can be applied. Especially in the machine industry where Industry 4.0 is one of the most important topics—including condition monitoring, predictive maintenance, and automated data analyses—data are limited and costly. In this work, the conventional and hybrid approach are compared to the application of ball bearing fault classification. The dataset contains 12 different classes (11 with faults and 1 undamaged). For each approach, two different LSTM (Long Short-Term Memory) models are developed and trained on various training sets (different sensors). The hybrid model is realised by adding physical knowledge through applying fast Fourier transformation and frequency selection to the raw data. This study shows that the additional physical knowledge in the hybrid model results in a better performance of the hybrid machine learning than the conventional.