license:apache-2.0 size_categories:100K<n<1M format:parquet library:datasets library:pandas library:mlcroissant library:polars region:us ethercat dcdiff anomaly
---
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

<!-- Provide a quick summary of the dataset. -->

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).

## Dataset Details

The data frame has two columns consisting of "Timestamp" and "DcDiffAvg".

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*.

For detail information and source code, find the following link.
https://github.com/kyoungje/WMXAnomalyDetection/tree/main


### Dataset Description


## Uses

Add Github notebook link
<!-- Address questions around how the dataset is intended to be used. -->