{"data":{"id":"10.5281/zenodo.15277167","type":"dois","attributes":{"doi":"10.5281/zenodo.15277167","prefix":"10.5281","suffix":"zenodo.15277167","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Carneiro, Davide","nameType":"Personal","givenName":"Davide","familyName":"Carneiro","affiliation":["Polytechnic Institute of Porto","INESC TEC"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-6650-0388","nameIdentifierScheme":"ORCID"}]},{"name":"Pinto Torres, Diogo","nameType":"Personal","givenName":"Diogo","familyName":"Pinto Torres","affiliation":["Polytechnic Institute of Porto"],"nameIdentifiers":[{"nameIdentifier":"0009-0000-1829-0649","nameIdentifierScheme":"ORCID"}]},{"name":"Peixoto, Eduardo","nameType":"Personal","givenName":"Eduardo","familyName":"Peixoto","affiliation":["Polytechnic Institute of Porto"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-5998-7960","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Real and Synthetic Data for Industrial Anomaly Detection in Injection Molding"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Industrial machine learning"},{"subject":"Fault detection"},{"subject":"Binary classification"},{"subject":"Maintenance prediction"},{"subject":"Trend analysis"},{"subject":"Sensor data processing"},{"subject":"Real-time monitoring"},{"subject":"Performance indicators"},{"subject":"Transfer Learning"},{"subject":"Zero-shot anomaly detection"},{"subject":"Frugal AI"}],"contributors":[],"dates":[{"date":"2026-04-09","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19482080","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.15277168","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"2.0.0","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Overview\n\nThis collection contains a blend of real-world and synthetic datasets designed for industrial anomaly detection. The centerpiece is Real Molding, a dataset collected directly from a real industrial injection molding machine. This real dataset is supported by five synthetic datasets (Lines A through E). These synthetic lines simulate varying operational conditions, from highly stable environments to turbulent regimes, providing a diverse historical pool for transfer learning, frugal AI, and zero-shot anomaly detection research.\n\nData Structure\n\nReal Molding Format\n\nThe real-world dataset captures specific process variables from the injection molding cycle:\n\n\n\ntimestamp: Date and time of measurement.\n\nInjection Time: Physical process variable.\n\nPlastification Time: Physical process variable.\n\nCycle Time: Physical process variable.\n\nCushion: Physical process variable.\n\nMax Pressure: Physical process variable.\n\nlabel: Binary indicator (0 = normal operation, 1 = genuine process deviation anomaly).\n\n\nSynthetic Lines Format\n\nThe synthetic datasets share a generalized sensor feature space:\n\n\n\ntimestamp: Date and time of measurement.\n\nTemperature: Process temperature.\n\nPressure: Process pressure.\n\nElapsed_time (Lines A and B only): Machine runtime.\n\nlabel: Binary indicator (0 = normal operation, 1 = anomaly).\n\n\nDataset Descriptions\n\n\n\nReal Molding (Industrial Target):\n\n\n\nSource: Real industrial injection molding machine.\n\nRecords: 2,999 production cycles.\n\nFeatures: 5 physical variables.\n\nCharacteristics: Realistic class imbalance representing genuine process deviations with complex, entangled feature distributions.\n\nAnomalies: 92 anomalies (3.06% rate).\n\nSize: 83.37KB\n\n\n\nLine A (Stable/Large):\n\n\n\nRecords: 10,000.\n\nFeatures: 3 variables (Temperature, Pressure, Elapsed Time).\n\nCharacteristics: Simulates a stable production line with low noise and distinct anomaly peaks, serving as a clean knowledge source.\n\nAnomalies: 18 anomalies (0.18% rate).\n\nSize: 775.68KB\n\n\n\nLine B (Balanced):\n\n\n\nRecords: 5,000.\n\nFeatures: 3 variables (Temperature, Pressure, Elapsed Time).\n\nCharacteristics: Represents a standard baseline with moderate noise levels.\n\nAnomalies: 50 anomalies (1.00% rate).\n\nSize: 390.71KB\n\n\n\nLine C (Turbulent):\n\n\n\nRecords: 5,000.\n\nFeatures: 2 variables (Temperature, Pressure).\n\nCharacteristics: Highly volatile process with significant noise and extreme class overlap.\n\nAnomalies: 200 anomalies (4.00% rate).\n\nSize: 295.02KB\n\n\n\nLine D (Noisy/Sparse):\n\n\n\nRecords: 5,000.\n\nFeatures: 2 variables (Temperature, Pressure).\n\nCharacteristics: Noisy conditions with a very low frequency of anomalies, challenging the detection process.\n\nAnomalies: 15 anomalies (0.30% rate).\n\nSize: 295.10KB\n\n\n\nLine E (Clean):\n\n\n\nRecords: 5,000.\n\nFeatures: 2 variables (Temperature, Pressure).\n\nCharacteristics: Highly controlled process with low noise and well-defined anomaly signatures.\n\nAnomalies: 25 anomalies (0.50% rate).\n\nSize: 295.08KB\n\n\n\n\nData Statistics\n\n\n\n\nSynthetic Datasets\nTemperature Range\nPressure Range\nElapsed Time Range\n% of Anomalies\n\n\nLineA_Stable_10K\n~179-180\n~159-160\n~34-35\n0.18%\n\n\nLineB_Flux\n~188-191\n~19-20\n~19-20\n1.00%\n\n\nLineC_Turbulent\n~196-210\n~97-103\nN/A\n4.00%\n\n\nLineD_SpikeControl\n~196-202\n~97-102\nN/A\n0.30%\n\n\nLineE_SmoothRun\n~199-200\n~99-100\nN/A\n0.50%\n\n\n\n\nSuggested Applications\n\n\n\nZero-shot anomaly detection and transfer learning across heterogeneous feature spaces;\n\nModel retrieval and historical model reuse for resource-constrained edge environments (Frugal AI);\n\nTime series analysis and handling of severe class imbalances in streaming data;\n\nBenchmarking meta-learning and algorithm selection techniques for industrial IoT;\n\nComparative analysis of domain shifts between synthetic proxies and real-world industrial targets.\n\n\n \n\nContact\n\n\n\nDavide Carneiro\n\ndavide.r.carneiro@inesctec.pt\n\nEscola Superior de Tecnologia e Gestão, Instituto Politécnico do Porto, 4610-156 Felgueiras, Portugal\n\nINESC TEC, R. Dr. Roberto Frias, 4200-465 Porto, Portugal","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"PRODUTECH R3","funderName":"Polytechnic Institute of Porto","awardNumber":"C645808870-00000067","funderIdentifier":"10.13039/501100007157","funderIdentifierType":"Crossref Funder ID"}],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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    <creator>
      <creatorName nameType="Personal">Carneiro, Davide</creatorName>
      <givenName>Davide</givenName>
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      <affiliation affiliationIdentifier="https://ror.org/05fa8ka61" affiliationIdentifierScheme="ROR">INESC TEC</affiliation>
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      <creatorName nameType="Personal">Pinto Torres, Diogo</creatorName>
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      <givenName>Eduardo</givenName>
      <familyName>Peixoto</familyName>
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      <affiliation affiliationIdentifier="https://ror.org/04988re48" affiliationIdentifierScheme="ROR">Polytechnic Institute of Porto</affiliation>
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  <titles>
    <title>Real and Synthetic Data for Industrial Anomaly Detection in Injection Molding</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2026</publicationYear>
  <resourceType resourceTypeGeneral="Dataset"/>
  <subjects>
    <subject>Industrial machine learning</subject>
    <subject>Fault detection</subject>
    <subject>Binary classification</subject>
    <subject>Maintenance prediction</subject>
    <subject>Trend analysis</subject>
    <subject>Sensor data processing</subject>
    <subject>Real-time monitoring</subject>
    <subject>Performance indicators</subject>
    <subject>Transfer Learning</subject>
    <subject>Zero-shot anomaly detection</subject>
    <subject>Frugal AI</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2026-04-09</date>
  </dates>
  <language>en</language>
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  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Overview

This collection contains a blend of real-world and synthetic datasets designed for industrial anomaly detection. The centerpiece is Real Molding, a dataset collected directly from a real industrial injection molding machine. This real dataset is supported by five synthetic datasets (Lines A through E). These synthetic lines simulate varying operational conditions, from highly stable environments to turbulent regimes, providing a diverse historical pool for transfer learning, frugal AI, and zero-shot anomaly detection research.

Data Structure

Real Molding Format

The real-world dataset captures specific process variables from the injection molding cycle:



timestamp: Date and time of measurement.

Injection Time: Physical process variable.

Plastification Time: Physical process variable.

Cycle Time: Physical process variable.

Cushion: Physical process variable.

Max Pressure: Physical process variable.

label: Binary indicator (0 = normal operation, 1 = genuine process deviation anomaly).


Synthetic Lines Format

The synthetic datasets share a generalized sensor feature space:



timestamp: Date and time of measurement.

Temperature: Process temperature.

Pressure: Process pressure.

Elapsed_time (Lines A and B only): Machine runtime.

label: Binary indicator (0 = normal operation, 1 = anomaly).


Dataset Descriptions



Real Molding (Industrial Target):



Source: Real industrial injection molding machine.

Records: 2,999 production cycles.

Features: 5 physical variables.

Characteristics: Realistic class imbalance representing genuine process deviations with complex, entangled feature distributions.

Anomalies: 92 anomalies (3.06% rate).

Size: 83.37KB



Line A (Stable/Large):



Records: 10,000.

Features: 3 variables (Temperature, Pressure, Elapsed Time).

Characteristics: Simulates a stable production line with low noise and distinct anomaly peaks, serving as a clean knowledge source.

Anomalies: 18 anomalies (0.18% rate).

Size: 775.68KB



Line B (Balanced):



Records: 5,000.

Features: 3 variables (Temperature, Pressure, Elapsed Time).

Characteristics: Represents a standard baseline with moderate noise levels.

Anomalies: 50 anomalies (1.00% rate).

Size: 390.71KB



Line C (Turbulent):



Records: 5,000.

Features: 2 variables (Temperature, Pressure).

Characteristics: Highly volatile process with significant noise and extreme class overlap.

Anomalies: 200 anomalies (4.00% rate).

Size: 295.02KB



Line D (Noisy/Sparse):



Records: 5,000.

Features: 2 variables (Temperature, Pressure).

Characteristics: Noisy conditions with a very low frequency of anomalies, challenging the detection process.

Anomalies: 15 anomalies (0.30% rate).

Size: 295.10KB



Line E (Clean):



Records: 5,000.

Features: 2 variables (Temperature, Pressure).

Characteristics: Highly controlled process with low noise and well-defined anomaly signatures.

Anomalies: 25 anomalies (0.50% rate).

Size: 295.08KB




Data Statistics




Synthetic Datasets
Temperature Range
Pressure Range
Elapsed Time Range
% of Anomalies


LineA_Stable_10K
~179-180
~159-160
~34-35
0.18%


LineB_Flux
~188-191
~19-20
~19-20
1.00%


LineC_Turbulent
~196-210
~97-103
N/A
4.00%


LineD_SpikeControl
~196-202
~97-102
N/A
0.30%


LineE_SmoothRun
~199-200
~99-100
N/A
0.50%




Suggested Applications



Zero-shot anomaly detection and transfer learning across heterogeneous feature spaces;

Model retrieval and historical model reuse for resource-constrained edge environments (Frugal AI);

Time series analysis and handling of severe class imbalances in streaming data;

Benchmarking meta-learning and algorithm selection techniques for industrial IoT;

Comparative analysis of domain shifts between synthetic proxies and real-world industrial targets.


 

Contact



Davide Carneiro

davide.r.carneiro@inesctec.pt

Escola Superior de Tecnologia e Gestão, Instituto Politécnico do Porto, 4610-156 Felgueiras, Portugal

INESC TEC, R. Dr. Roberto Frias, 4200-465 Porto, Portugal</description>
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      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100007157</funderIdentifier>
      <awardNumber>C645808870-00000067</awardNumber>
      <awardTitle>PRODUTECH R3</awardTitle>
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