|
--- |
|
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. --> |
