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Multi-Scale and Multi-Rate Neural Networks for Intelligent Bearing Fault Diagnosis System

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thesis
posted on 2022-12-15, 19:07 authored by Xiaofan LiuXiaofan Liu

 Roller bearing is one of the machine industry’s common components. The roller bearing operation status is usually related to production efficiency. Failure of bearings during operation will cause downtime and severe economic losses. To prevent this situation, the proposal of effective bearing fault diagnosis methods has become a popular research topic. This thesis research first validates several popular bearing diagnosis methods based on signal processing and machine learning. Second, a novel signal feature extraction method called sparse wavelet packet transform (WPT) decomposition and a corresponding feature learning model called multi-scale and multi-rate convolutional neural network (MSMR-CNN) are proposed. Finally, the proposed method is verified using both Case Western Reserve University (CWRU) dataset and the self-collected dataset. The results demonstrate that our proposed MSMR-CNN method achieves higher performance of bearing fault classification accuracy in comparison with the methods which are recently proposed by the other researchers using machine learning and neural networks .

History

Degree Type

Master of Science

Department

Electrical and Computer Engineering

Campus location

Hammond

Advisor/Supervisor/Committee Chair

Lizhe Tan

Additional Committee Member 2

Quamar Niyaz

Additional Committee Member 3

Khair AI Shamaileh