# Machine Fault Classification using Hamiltonian Neural Networks

arXiv: https://arxiv.org/abs/2301.02243 · HTML: https://mlsanity.com/paper/2301.02243/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 4 Jan 2023.

## Builds on (most cited first, 1 of 1)

- [Hamiltonian Neural Networks](https://mlsanity.com/paper/1906.01563/) (2019)

## Built on by (most cited first, 3 of 3)

- [Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity](https://mlsanity.com/paper/2302.01955/) (2023)
- [A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection](https://mlsanity.com/paper/2401.11860/) (2024)
- [Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy](https://mlsanity.com/paper/2603.29135/) (2026)

## Abstract (arXiv)

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).