Welcome to the 2027 DX and Safeprocess Benchmarks page

This is the main page for the 2027 DX and Safeprocess competitions. In the competitions, it is possible to participate in different benchmarks and it is possible to participate in one or multiple benchmarks.

If you are interested in or intend to participate in any of the benchmarks, please send a notification email to the corresponding contact persons for the benchmarks.

Safeprocess submission deadline: The deadline for submitting your solutions is April 29, 2027.

All benchmarks have the same deadline and your solutions should be submitted to the corresponding contact person.

Available benchmarks

A common diagnosis system interface

The developed diagnosis system solution must be implemented in Python. A Docker image is available for each benchmark where the diagnosis system shall be implemented. The diagnosis system shall be implemented with a specific input/output interface based on a DiagnosisSystem class.

At each time instance, a new sample is provided to the diagnosis system and a diagnosis output shall be provided from the diagnosis system. All benchmarks are using the same implementation environment to simplify participation in multiple competitions. Please look into each benchmark for a more detailed description of each input/output interface.

Brief descriptions of benchmarks

LiU-ICE

The LiU-ICE benchmark covers some challenging problems of fault diagnosis of technical systems. The diagnosis system needs to identify the faulty component as fast and accurately as possible while avoiding misclassifications and falsely rejecting the true diagnosis. The objective of the competition is to address these challenges by designing a diagnosis system for the air path of an internal combustion engine. It is a challenging system because of its dynamic non-linear behavior and wide operating range. A state-of-the-art structural model of the system is provided together with training data from different fault scenarios. The set of available actuator and sensor signals corresponds to the standard signals that are available in a commercial vehicle.

More information and downloadable resources can be found here:

Contact person: Daniel Jung

SLIDe

SLIDe (Steam Line Intrusion Detection Benchmark) benchmark is devoted to the analysis of diagnostic algorithms for the detection and isolation of process faults and the detection of cyberattacks for a simulated fragment of the steam line of a fluidized bed boiler including the third and fourth stage of superheaters. It includes challenging scenarios including sensor, actuator, and technological components faults as well as cyber-attacks. To reflect the industrial nature of the benchmark, participants will only have a qualitative description of the process with a list of measurements and a few archival datasets representing different operating conditions, but only for fault-free and attack-free states.

More information and downloadable resources can be found here:

Contact person: Michał Syfert

COMIC

The COMIC benchmark targets the diagnosis of combinatorial circuits known from the ISCAS benchmark suites (see https://ddd.fit.cvut.cz/www/prj/Benchmarks/ for some info on digital design benchmarks). Due to the complexity of diagnosis problems for larger circuits, the contestants are expected to compute all diagnoses of minimal cardinality only. The benchmark contains a set of diagnosis problems that must be computed on the evaluation platform within a given time limit.

More information and downloadable resources can be found on Github:

Contact person: Johan de Kleer or Ingo Pill

LUMEN

LUMEN (Liquid Upper stage demonstrator Engine) is a modular pump-fed liquid oxygen (LOX) and liquid methane (LNG) rocket engine developed by the Institute of Space Propulsion of the German Aerospace Center (DLR). This benchmark focuses on the fuel turbopump subsystem of the rocket engine and addresses key challenges encountered in safety-critical systems, such as the lack of experimental data from faulty operation. The goal of this benchmark is to utilize information from a simulation model with uncertain parameter and limited experimental data from nominal operation to enable the diagnosis system to perform effectively under realistic operating conditions.

More information and downloadable resources will be provided.

Contact person: Eldin Kurudzija