Machine Fault Classification Using Hamiltonian Neural Networks

Jeremy Shen, Jawad Chowdhury, Sourav Banerjee, Gabriel Terejanu

2023

Abstract

A new approach is introduced to classify faults in rotating machinery based on the total energy signature estimated from sensor measurements. The overall goal is to go beyond using black-box models and incorporate additional physical constraints that govern the behavior of mechanical systems. Observational data is used to train Hamiltonian neural networks that describe the conserved energy of the system for normal and various abnormal regimes. The estimated total energy function, in the form of the weights of the Hamiltonian neural network, serves as the new feature vector to discriminate between the faults using off-the-shelf classification models. The experimental results are obtained using the MaFaulDa database, where the proposed model yields a promising area under the curve (AUC) of 0.78 for the binary classification (normal vs abnormal) and 0 .84 for the multi-class problem (normal, and 5 different abnormal regimes).

Paper Citation

in Harvard Style

Shen J., Chowdhury J., Banerjee S. and Terejanu G. (2023). Machine Fault Classification Using Hamiltonian Neural Networks. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 474-480. DOI: 10.5220/0011746800003411

in Bibtex Style

@conference{icpram23,
author={Jeremy Shen and Jawad Chowdhury and Sourav Banerjee and Gabriel Terejanu},
title={Machine Fault Classification Using Hamiltonian Neural Networks},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={474-480},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011746800003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Machine Fault Classification Using Hamiltonian Neural Networks
SN - 978-989-758-626-2
AU - Shen J.
AU - Chowdhury J.
AU - Banerjee S.
AU - Terejanu G.
PY - 2023
SP - 474
EP - 480
DO - 10.5220/0011746800003411