{"url":"/dataset/cwru-bearing-dataset","name":"CWRU Bearing Dataset","full_name":null,"description_markdown":"Data was collected for normal bearings, single-point drive end and fan end defects.  Data was collected at 12,000 samples/second and at 48,000 samples/second for drive end bearing experiments.  All fan end bearing data was collected at 12,000 samples/second.","description_withheld":null,"homepage":"https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":{"name":"Unknown","url":null},"modalities":[],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["CWRU Bearing Dataset"],"data_loaders":[],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/classification-on-cwru-bearing-dataset","task":"Classification","dataset_variant":"CWRU Bearing Dataset","rows":4,"metrics":["10 fold Cross validation"],"first_row_in_archive_order":{"model":"MixMamba-Fewshot","paper":"/paper/mixmamba-fewshot-mamba-and-attention-mixer","metrics":{"10 fold Cross validation":"99.93"},"code_links":[{"title":"linhthan216/MixMamba-Fewshot","url":"https://github.com/linhthan216/MixMamba-Fewshot"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/mixmamba-fewshot-mamba-and-attention-mixer","title":"Mixmamba-fewshot: mamba and attention mixer-based method with few-shot learning for bearing fault diagnosis","date":"2025-02-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/few-shot-bearing-fault-diagnosis-via","title":"Few-Shot Bearing Fault Diagnosis Via Ensembling Transformer-Based Model With Mahalanobis Distance Metric Learning From Multiscale Features","date":"2024-03-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/faultnet-a-deep-convolutional-neural-network","title":"FaultNet: A Deep Convolutional Neural Network for bearing fault classification","date":"2020-10-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/bearing-fault-diagnosis-base-on-multi-scale","title":"Bearing Fault Diagnosis Base on Multi-scale CNN and LSTM Model","date":"2020-06-05","rows_on_this_dataset":1,"code_links":3,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}