FaultNet: A Deep Convolutional Neural Network for bearing fault classification

TL;DR

This paper introduces FaultNet, a deep convolutional neural network that combines signal processing techniques to improve accuracy in classifying bearing faults from vibration data.

Contribution

The work proposes a novel CNN architecture called FaultNet with channel stacking of mean and median signals to enhance fault classification accuracy.

Findings

FaultNet achieves high accuracy in bearing fault classification.

Channel stacking improves feature extraction and model performance.

Analysis of different signal processing methods impacts classification results.

Abstract

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.