Purdue University Graduate School
Browse
GRAPH NEURAL NETWORKS BASED ON MULTI-RATE SIGNAL DECOMPOSITION FOR BEARING FAULT DIAGNOSIS.pdf (2.45 MB)

GRAPH NEURAL NETWORKS BASED ON MULTI-RATE SIGNAL DECOMPOSITION FOR BEARING FAULT DIAGNOSIS.pdf

Download (2.45 MB)
thesis
posted on 2023-05-12, 12:40 authored by Guanhua ZhuGuanhua Zhu

Roller bearings are the common components used in the mechanical systems for mechanical processing and production. The running state of roller bearings often determines the machining accuracy and productivity on a manufacturing line. Roller bearing failure may lead to the shutdown of production lines, resulting in serious economic losses. Therefore, the research on roller bearing fault diagnosis has a great value. This thesis research first proposes a method of signal frequency spectral resampling to tackle the problem of bearing fault detection at different rotating speeds using a single speed dataset for training the network such as the one dimensional convolutional neural network (1D CNN). Second, this research work proposes a technique to connect the graph structures constructed from spectral components of the different bearing fault frequency bands into a sparse graph structure, so that the fault identification can be carried out effectively through a graph neural network in terms of the computation load and classification rate. Finally, the frequency spectral resampling method for feature extraction is validated using our self-collected datasets. The performance of the graph neural network with our proposed sparse graph structure is validated using the Case Western Reserve University (CWRU) dataset as well as our self-collected datasets. The results show that our proposed method achieves higher bearing fault classification accuracy than those recently proposed by other researchers using machine learning approaches and neural networks.

History

Degree Type

  • Master of Science in Electrical and Computer Engineering

Department

  • Electrical and Computer Engineering

Campus location

  • Hammond

Advisor/Supervisor/Committee Chair

Dr. Lizhe Tan

Additional Committee Member 2

Dr. Quamar Niyaz

Additional Committee Member 3

Dr. Khair AI Shamaileh

Usage metrics

    Categories

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC