PIADE dataset contains data from five industrial packaging machines: Machine s_1: from 2020-01-01 14:00:00 to 2021-12-31 13:00:00 Machine s_2: from 2020-06-17 08:00:00 to 2021-12-31 07:00:00 Machine s_3: from 2020-10-07 12:00:00 to 2022-01-01 23:00:00 Machine s_4: from 2020-01-01 01:00:00 to 2022-01-01 23:00:00 Machine s_5: from 2020-01-20 08:00:00 to 2022-01-01 12:00:00 ## Raw Data Each row represents a production interval, with the following schema: interval_start: start of the production interval equipment_ID: equipment identifier alarm: alarm code of the active stop reason, if it occurred type: idle, production, downtime, performance_loss or scheduled_downtime start: start of the production interval ...
This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection (...
The use of data driven techniques is popular in smart manufacturing. Machine learning, statistics or...
Tronrud engineering’s packaging machines have occurrences of lost products that exceed the expectati...
The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts o...
The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts o...
This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted...
Within the industrial sector, there has not been much research from the scientific community on how ...
In this paper we propose a new method to assist in labeling data arriving from fast running processe...
o build, run, and maintain reliable manufacturing machines, the condition of their components has to...
We outline an anomaly detection method for industrial control systems (ICS) that combines the analys...
Industrie 4.0 environments generate an unprecedented amount of production data. This is due to the r...
The speed at which a manufacturing company analyzes big data and reacts to the market trends can be ...
This work presents a new methodology for machine tools anomaly detection via operational data proce...
Anomaly detection is a crucial aspect for both safety and efficiency of modern process industries. ...
Forecasting of product quality by means of anomaly detection is crucial in real-world applications s...
This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection (...
The use of data driven techniques is popular in smart manufacturing. Machine learning, statistics or...
Tronrud engineering’s packaging machines have occurrences of lost products that exceed the expectati...
The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts o...
The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts o...
This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted...
Within the industrial sector, there has not been much research from the scientific community on how ...
In this paper we propose a new method to assist in labeling data arriving from fast running processe...
o build, run, and maintain reliable manufacturing machines, the condition of their components has to...
We outline an anomaly detection method for industrial control systems (ICS) that combines the analys...
Industrie 4.0 environments generate an unprecedented amount of production data. This is due to the r...
The speed at which a manufacturing company analyzes big data and reacts to the market trends can be ...
This work presents a new methodology for machine tools anomaly detection via operational data proce...
Anomaly detection is a crucial aspect for both safety and efficiency of modern process industries. ...
Forecasting of product quality by means of anomaly detection is crucial in real-world applications s...
This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection (...
The use of data driven techniques is popular in smart manufacturing. Machine learning, statistics or...
Tronrud engineering’s packaging machines have occurrences of lost products that exceed the expectati...