Tosato, Diego and Convento, Enrico and Masiero, Chiara and Susto, Gian Antonio and Beghi, Alessandro (2022) Packaging Industry Anomaly DEtection (PIADE) Dataset. [Data Collection]
Collection description
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 end: end of the production interval elapsed: duration of the production interval pi: input packages po: output packages speed: speed (packages per hour) There are 133 different types of alerts, and 429394 rows.<br> ## Sequences (1h) data For each piece of equipment, we define sequences of length = 1 hour and we aggregate raw interval data as follows: 'equipment_ID': machine identifier '#changes': changes in machine state '%downtime': time spent in 'downtime' state '%idle': time spent in 'idle' state '%performance_loss': time spent in 'performance loss' state '%production': time spent in production '%scheduled_downtime': time spent in scheduled downtime 'count_sum': sum of all alarm occurrences 'A_<XXX>': counter of alarm <XXX> occurrences '<state1>/<state2>': number of transitions from <state1> to <state2>
| DOI: |  10.5281/zenodo.7071746 |
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| Keywords: | Industry 4.0, Anomaly Detection, Alarm Forecasting | ||||||||||||||||||
| Subjects: | Physical:Sciences and Engineering > Products and Processes Engineering: Product design, process design and control, construction methods, civil engineering, energy processes, material engineering > Production technology, process engineering | ||||||||||||||||||
| Department: | Departments > Dipartimento di Ingegneria dell'Informazione (DEI) | ||||||||||||||||||
| Depositing User: | Research Data Unipd | ||||||||||||||||||
| Date Deposited: | 16 Dec 2024 20:54 | ||||||||||||||||||
| Last Modified: | 16 Dec 2024 20:59 | ||||||||||||||||||
| Creators/Authors: |
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| Type of data: | Text | ||||||||||||||||||
| Research funder: | European Commission | ||||||||||||||||||
| Grant number: | 951771 | ||||||||||||||||||
| Collection period: |
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| Resource language: | en | ||||||||||||||||||
| Metadata language: | en | ||||||||||||||||||
| Additional information: | The collection of this dataset has been partially supported by the Regione Veneto project VIR2EM (VIrtualization and Remotization for Resilient and Efficient Manufacturing, Virtualizzazione e remotizzazione per una manifattura efficiente e resiliente) | ||||||||||||||||||
| Publisher: | Zenodo | ||||||||||||||||||
| Status: | Published | ||||||||||||||||||
| Date: | 2022 | ||||||||||||||||||
| Date type: | Publication | ||||||||||||||||||
| Copyright holders: | The Authors | ||||||||||||||||||
| URI: | https://researchdata.cab.unipd.it/id/eprint/1459 |
orcid.org/0000-0001-9198-0750
