| --- |
| license: apache-2.0 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| dataset_info: |
| features: |
| - name: Timestamp |
| dtype: timestamp[ns, tz=+09:00] |
| - name: DcDiffAvg |
| dtype: int64 |
| splits: |
| - name: train |
| num_bytes: 1600752 |
| num_examples: 100047 |
| download_size: 1452329 |
| dataset_size: 1600752 |
| tags: |
| - ethercat |
| - dcdiff |
| - anomaly |
| pretty_name: wmx_master_stat_dcdiff_norma |
| --- |
| # Dataset Card for Dataset Name |
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| <!-- Provide a quick summary of the dataset. --> |
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| This dataset card aims to train an LSTM autoencoder model to detect anomalies of DC diff statistics calculated by the [WMX Ethercat master](https://www.movensys.com/en/products/software_motion_control/wmx_en). |
|
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| ## Dataset Details |
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| The data frame has two columns consisting of "Timestamp" and "DcDiffAvg". |
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| Every cycle is done, the average time interval to the next DC clock for each cycle is cacluated in ns, and this value shows a peculiar sawtooth pattern as follows. |
| Using this dataset **the autoencoder model** can be trained *to detect anomalies in case of unstable communication between the master(Main device) and sub-devices*. |
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| For detail information and source code, find the following link. |
| https://github.com/kyoungje/WMXAnomalyDetection/tree/main |
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| ### Dataset Description |
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| ## Uses |
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| Add Github notebook link |
| <!-- Address questions around how the dataset is intended to be used. --> |
