# The LiU-ICE Benchmark -- An Industrial Fault Diagnosis Case Study

arXiv: https://arxiv.org/abs/2408.13269 · HTML: https://mlsanity.com/paper/2408.13269/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2024.

## Built on by (most cited first, 1 of 1)

- [Probabilistic Machine Learning for Uncertainty-Aware Diagnosis of Industrial Systems](https://mlsanity.com/paper/2509.18810/) (2025)

## Abstract (arXiv)

This paper presents the LiU-ICE fault diagnosis benchmark. The purpose of the benchmark is to support fault diagnosis research by providing data and a model of an industrially relevant system. Data has been collected from an internal combustion engine test bench operated in both nominal and faulty modes. A state-of-the-art model of the air path through an internal combustion engine with unknown parameters is provided. This benchmark has previously been used in a competition at the 12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (Safe Process) 2024, Ferrara, Italy.