Papers › FaultNet: A Deep Convolutional Neural Network for bearing fault classification

FaultNet: A Deep Convolutional Neural Network for bearing fault classification

5 Oct 2020arXiv:2010.02146archive 2025-07-28

Rishikesh Magar, Lalit Ghule, Junhan Li, Yang Zhao, Amir Barati Farimani

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.

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BaratiLab/FaultNet officialmentioned in papermentioned on GitHub report

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Tasks

BIG-bench Machine Learning Classification Fault Detection General Classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Classification	CWRU Bearing Dataset	CNN	10 fold Cross validation	7	#4 of 4	Archive leaderboard	report

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