Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Fault Diagnosis in REDNet Model Space
Authors: Xiren Zhou, Ziyu Tang, Shikang Liu, Ao Chen, Xiangyu Wang, Huanhuan Chen
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several datasets demonstrate its effectiveness. Experimental results on various datasets demonstrate its effectiveness and practicality, especially in data-scarce scenarios with only limited labeled samples. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology in Section 3, using textual descriptions, mathematical equations, and figures to illustrate concepts and processes, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. While it mentions "Implementation from scikit-learn: https://scikit-learn.org", this refers to a third-party library used, not the authors' own code release. |
| Open Datasets | Yes | The experiments employ four well-known FD datasets, including CWRU4, SMD5, TBV6 and GFD7. 4https://engineering.case.edu/bearingdatacenter 5https://github.com/Net Man AIOps/Omni Anomaly 6https://data.mendeley.com/datasets/fm6xzxnf36/2 7https://www.kaggle.com/datasets/brjapon/ gearbox-fault-diagnosis |
| Dataset Splits | Yes | Specifically, to address scenarios with limited labeled data, we constrain the training data to just 50 samples per category across all utilized datasets, with the remaining serving for testing. |
| Hardware Specification | Yes | The experiments are conducted with an Intel Xeon E5-2650 v3 CPU and NVIDIA Ge Force RTX 3090 GPU, running MATLAB R2021a and Python 3.8. |
| Software Dependencies | Yes | The experiments are conducted with an Intel Xeon E5-2650 v3 CPU and NVIDIA Ge Force RTX 3090 GPU, running MATLAB R2021a and Python 3.8. Implementation from scikit-learn: https://scikit-learn.org. |
| Experiment Setup | Yes | The default settings of REDNet include a spectral radius of 0.7 and a reservoir size of 60. In the REDNet model space, SVM is adopted3 by default for classification, and other classifiers are subsequently evaluated. |