{"url":"/dataset/voraus-ad","name":"voraus-AD","full_name":null,"description_markdown":"voraus-AD contains machine data of a collaborative robot, which moves a can by performing an industrial pick-and-place task. The samples consist of time series of machine data, each recorded over one pick-and-place operation. As usual in anomaly detection, the training set contains only normal data, which includes regular samples without anomalies. The test set contains both, normal data and anomalies, including 12 diverse anomaly types. In order to create a realistic scenario, we have divided the normal data into training and test data as follows: Up to a certain period of time, only training data including 948 samples was recorded. Subsequently, recordings of anomalies (755 samples) and normal data (419 samples) for the test set were taken alternately. This simulates a real application where training data would be recorded first in the same way to train the model before the test case occurs.\r\nTo exclude temperature effects, we let robots warm up for half an hour before each recording.","description_withheld":null,"homepage":"https://github.com/vorausrobotik/voraus-ad-dataset","introduced_date":"2023-11-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-voraus-ad-dataset-for-anomaly-detection","title":"The voraus-AD Dataset for Anomaly Detection in Robot Applications","first_author":"Jan Thieß Brockmann","url":null},"license":{"name":"CC BY-NC-SA 4.0 License","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"}],"languages":[],"variants":["voraus-AD"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-voraus-ad","task":"Anomaly Detection","dataset_variant":"voraus-AD","rows":3,"metrics":["Avg. Detection AUROC"],"first_row_in_archive_order":{"model":"MVT-Flow","paper":"/paper/the-voraus-ad-dataset-for-anomaly-detection","metrics":{"Avg. Detection AUROC":"93.6"},"code_links":[{"title":"vorausrobotik/voraus-ad-dataset","url":"https://github.com/vorausrobotik/voraus-ad-dataset"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/the-voraus-ad-dataset-for-anomaly-detection","title":"The voraus-AD Dataset for Anomaly Detection in Robot Applications","date":"2023-11-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph-augmented-normalizing-flows-for-anomaly-1","title":"Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series","date":"2022-02-16","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":24,"samples_ran":8,"samples_unverified":16,"pointer_only_for_licence":10,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-multimodal-anomaly-detector-for-robot","title":"A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder","date":"2017-11-02","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":1,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":34,"samples_ran":9,"samples_unverified":25,"pointer_only_for_licence":10,"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."}