# Isolation and Localization of Unknown Faults Using Neural Network-Based Residuals

arXiv: https://arxiv.org/abs/1910.05626 · HTML: https://mlsanity.com/paper/1910.05626/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

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

- [Analysis of Numerical Integration in RNN-Based Residuals for Fault Diagnosis of Dynamic Systems](https://mlsanity.com/paper/2305.04670/) (2023)
- [Probabilistic Machine Learning for Uncertainty-Aware Diagnosis of Industrial Systems](https://mlsanity.com/paper/2509.18810/) (2025)
- [An Explainable GNN Framework for Component-Level Anomaly Diagnosis](https://mlsanity.com/paper/2608.09246/) (2026)

## Abstract (arXiv)

Localization of unknown faults in industrial systems is a difficult task for data-driven diagnosis methods. The classification performance of many machine learning methods relies on the quality of training data. Unknown faults, for example faults not represented in training data, can be detected using, for example, anomaly classifiers. However, mapping these unknown faults to an actual location in the real system is a non-trivial problem. In model-based diagnosis, physical-based models are used to create residuals that isolate faults by mapping model equations to faulty system components. Developing sufficiently accurate physical-based models can be a time-consuming process. Hybrid modeling methods combining physical-based methods and machine learning is one solution to design data-driven residuals for fault isolation. In this work, a set of neural network-based residuals are designed by incorporating physical insights about the system behavior in the residual model structure. The residuals are trained using only fault-free data and a simulation case study shows that they can be used to perform fault isolation and localization of unknown faults in the system.