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README.md
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---
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license: apache-2.0
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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dataset_info:
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features:
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- name: Timestamp
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dtype: timestamp[ns, tz=+09:00]
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- name: DcDiffAvg
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dtype: int64
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splits:
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- name: train
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num_bytes: 1600752
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num_examples: 100047
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download_size: 1452329
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dataset_size: 1600752
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---
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license: apache-2.0
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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dataset_info:
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features:
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- name: Timestamp
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dtype: timestamp[ns, tz=+09:00]
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- name: DcDiffAvg
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dtype: int64
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splits:
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- name: train
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num_bytes: 1600752
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num_examples: 100047
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download_size: 1452329
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dataset_size: 1600752
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tags:
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- ethercat
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- dcdiff
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- anomaly
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pretty_name: wmx_master_stat_dcdiff
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---
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# 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.
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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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### Dataset Description
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## Uses
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Add Github notebook link
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<!-- Address questions around how the dataset is intended to be used. -->
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