Model-Based Diagnosis of Hybrid Systems Using Satisfiability Modulo Theory

Authors: Alexander Diedrich, Alexander Maier, Oliver Niggemann1452-1459

AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental For the experimental evaluation we use a simulation of the Tennessee Eastman process and a simulation of a four-tank model. We show that the presented approach is able to identify all injected faults. and Evaluation Empirical Evaluation Table 1 shows the experiments of the simulated four-tank model for constant input stream, the injected faults and whether or not the fault was detected.
Researcher Affiliation Collaboration Alexander Diedrich, Alexander Maier Fraunhofer IOSB-INA Fraunhofer Center for Machine Learning Lemgo, Germany alexander.diedrich@iosb-ina.fraunhofer.de alexander.maier@iosb-ina.fraunhofer.de Oliver Niggemann Institute Industrial IT Lemgo, Germany oliver.niggemann@hs-owl.de
Pseudocode No The paper does not contain any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any explicit statement or link regarding the public release of its source code.
Open Datasets Yes For the experimental evaluation we use a simulation of the Tennessee Eastman process and a simulation of a four-tank model. and The implementation of Downs et al. (Downs and Vogel 1993) was used which contains 20 different injected faults (process disturbances).
Dataset Splits No The paper discusses evaluating the algorithm at specific time-steps in the simulation ('time step 101'), but it does not provide details on traditional training, validation, or test dataset splits (percentages, counts, or predefined citations) for reproducibility.
Hardware Specification No The paper does not provide any specific hardware details (such as CPU, GPU, or memory specifications) used for running the experiments.
Software Dependencies No The paper mentions using 'the SMT solver z3' but does not specify its version number or any other software dependencies with version information.
Experiment Setup No The paper describes the simulation setup (e.g., 'constant input stream', '300 time-steps') and the theoretical framework, but it does not provide specific experimental setup details such as hyperparameters, optimization settings, or detailed model initialization values.