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--- |
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license: cc-by-4.0 |
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task_categories: |
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- time-series-forecasting |
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- tabular-classification |
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tags: |
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- bioprocess |
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- manufacturing |
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- anomaly-detection |
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- fault-detection |
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- batch-monitoring |
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- pharma |
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pretty_name: Golden Batch Sentinel Data |
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size_categories: |
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- 1M<n<10M |
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--- |
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# Golden Batch Sentinel Data |
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Benchmark datasets for process monitoring and fault detection in batch manufacturing. |
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## Datasets |
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### IndPenSim (Industrial Penicillin Simulation) |
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A 100,000L fermentation simulation with 100 batches and rich multivariate signals. |
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- **Source**: [Mendeley Data](https://data.mendeley.com/datasets/pdnjz7zz5x/2) |
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- **Paper**: [Modern day monitoring and control challenges...](https://doi.org/10.1016/j.compchemeng.2018.05.019) |
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- **Batches**: 100 (90 normal, 10 faulty) |
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- **Variables**: 37 process variables (Raman spectra excluded for efficiency) |
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- **Time resolution**: 0.2 hours |
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**Files:** |
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- `indpensim/batches.parquet` - Main batch data |
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- `indpensim/statistics.parquet` - Batch statistics and fault labels |
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### Tennessee Eastman Process (TEP) |
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The most common benchmark for fault detection in multivariate industrial processes. |
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- **Source**: [Harvard Dataverse](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6C3JR1) |
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- **Fault types**: 20 different fault scenarios |
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- **Variables**: 52 (41 measured + 11 manipulated) |
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**Files:** |
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- `tep/fault_free_train.parquet` - Normal operation (training) |
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- `tep/fault_free_test.parquet` - Normal operation (testing) |
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- `tep/faulty_train.parquet` - Faulty operation (training, all 20 faults) |
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- `tep/faulty_test.parquet` - Faulty operation (testing, all 20 faults) |
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## Usage |
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```python |
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from datasets import load_dataset |
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# Load IndPenSim |
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indpensim = load_dataset("foundation-models/golden-batch-sentinel-data", data_dir="indpensim") |
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# Load TEP |
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tep = load_dataset("foundation-models/golden-batch-sentinel-data", data_dir="tep") |
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# Or load specific files |
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import pandas as pd |
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from huggingface_hub import hf_hub_download |
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path = hf_hub_download( |
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repo_id="foundation-models/golden-batch-sentinel-data", |
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filename="indpensim/batches.parquet", |
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repo_type="dataset" |
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) |
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df = pd.read_parquet(path) |
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``` |
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## License |
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The original datasets are provided under their respective licenses: |
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- IndPenSim: CC BY 4.0 |
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- TEP: Public domain |
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This compilation is provided under CC BY 4.0. |
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## Citation |
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If you use this data, please cite the original papers: |
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```bibtex |
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@article{goldrick2019modern, |
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title={Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process}, |
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author={Goldrick, Stephen and others}, |
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journal={Computers \& Chemical Engineering}, |
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year={2019} |
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} |
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@article{downs1993plant, |
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title={A plant-wide industrial process control problem}, |
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author={Downs, James J and Vogel, Ernest F}, |
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journal={Computers \& Chemical Engineering}, |
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year={1993} |
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} |
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``` |
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