FaultNet: A Deep Convolutional Neural Network for bearing fault classification
This work addresses machine health monitoring for industrial systems, but it is incremental as it builds on existing methods with a novel channel stacking approach.
The authors tackled bearing fault classification using vibration signal data, proposing a convolutional neural network called FaultNet that achieves high accuracy by stacking Mean and Median channels to raw signals for feature extraction.
The increased presence of advanced sensors on the production floors has led to the collection of datasets that can provide significant insights into machine health. An important and reliable indicator of machine health, vibration signal data can provide us a greater understanding of different faults occurring in mechanical systems. In this work, we analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults. We also highlight the importance of using different signal processing methods and analyze their effect on accuracy for bearing fault detection. Apart from the traditional machine learning algorithms we also propose a convolutional neural network FaultNet which can effectively determine the type of bearing fault with a high degree of accuracy. The distinguishing factor of this work is the idea of channels proposed to extract more information from the signal, we have stacked the Mean and Median channels to raw signal to extract more useful features to classify the signals with greater accuracy.