Real and Synthetic Data for Industrial Anomaly Detection in Injection Molding

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 [e-mail] Escola Superior de Tecnologia e Gestão, Instituto Politécnico do Porto, 4610-156 Felgueiras, Portugal INESC TEC, R. Dr. Roberto Frias, 4200-465 Porto, Portugal