Admino739mjm7: Created page with "Timeseries dataset with pneumatic pressure and electrical current measurements, accompanied by 2 Jupyter notebooks for basic plotting. == Asset Description ==
Data obtained from a real-world discrete manufacturing shopfloor and containing measurements of pneumatic pressure and electrical current. The dataset contains 7 days of operation, spanning roughly 150 processed pieces.
== Asset Details == === Dataset Informat..."

2025-10-03T09:29:17Z

Created page with "Timeseries dataset with pneumatic pressure and electrical current measurements, accompanied by 2 Jupyter notebooks for basic plotting. == Asset Description == Data obtained from a real-world discrete manufacturing shopfloor and containing measurements of pneumatic pressure and electrical current. The dataset contains 7 days of operation, spanning roughly 150 processed pieces. == Asset Details == === Dataset Informat..."

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Timeseries dataset with pneumatic pressure and electrical current measurements, accompanied by 2 Jupyter notebooks for basic plotting. 

== Asset Description ==

Data obtained from a real-world discrete manufacturing shopfloor and containing measurements of pneumatic pressure and electrical current. The dataset contains 7 days of operation, spanning roughly 150 processed pieces.


== Asset Details ==
=== Dataset Information ===

The observed process is an end-of-line testing machine for consumer-grade small electric drive assembly (device under test – DUT). The machine takes several actions to evaluate each DUT, the measurements present in this dataset observe the pneumatic pressure powering actuators and the electrical current activating them. The data is pre-segmented using the testing machines internal state machine. Each segment corresponds to single action performed by the testing station in the process of manipulating the piece under observation.

The dataset repository contains also two Jupyter notebooks that demonstrate basic loading and plotting of the data.


== Usage ==

The dataset contains time series pre-segmented at points in time where the internal state-machine of the observed process changes, i.e. at points in time when the machine transitions from one operation to the next. The labels contained within the dataset enable the application of supervised learning algorithms such as time series classification, as well as validation of unsupervised approaches such as time series clustering. Dataset to be used in the design of time series algorithms aimed at non-invasive monitoring of (discrete) industrial processes.


=== Maturity===

The datasets are fully documented and available. 

=== Licence===
Open source, CC BY 4.0

== Resources ==
<ul>
<li>Dataset and Jupyter Notebooks available in [https://data.mendeley.com/datasets/ypzswhhzh9/3 Mendeley Data]</li>
<li>Provided by [https://www.ijs.si/ijsw/V001/JSI Jožef Stefan Institute (JSI)]</li>
</ul>

==== Acknowledgement====

''This work was funded partly by AI REDGIO 5.0 (101092069) and partly by the Slovenian Research and Innovation Agency (L2-4454, P2-0001). The dataset will be used in the AI REDGIO 5.0 Didactic Factory Pilot DFIII: Self-evolving monitoring systems for assembly production lines.''


== Relevant Categories==
[[Category:Dataset]][[Category:Jupyter Notebook]][[Category:Predictive Maintenance]][[Category:Machine Learning]][[Category:Quality Control]][[Category:Anomaly Detection]]

Pneumatic Pressure and Electrical Current Time Series - Revision history