Real and Synthetic Data for Industrial Anomaly Detection in Injection Molding 机器收录·待核验

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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

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落地页

https://doi.org/10.5281/zenodo.15277167

许可证

CC-BY-4.0 (置信:verified_official)

发布年份

2026

发布方

Zenodo

分发点