AI System for Industrial IoT (IIoT) — Monitoring and Analytics
IIoT differs from consumer IoT: criticality, hard real-time, legacy equipment without network interfaces, explosive zones (ATEX equipment), industrial protocol specifics.
IIoT AI Platform Architecture
Data Acquisition Layer: OPC-UA servers for modern equipment. Modbus TCP/RTU for legacy PLC. 4–20 mA converters with IoT gateways for analog equipment. Historian (OSIsoft PI, Aveva PI) as historical data source.
Edge Processing: Industrial computers (Siemens IPC, Advantech) or hardened Jetson Nano in IP67 enclosures. MQTT Sparkplug B for data standardization. Local ML inference for latency-critical tasks.
AI Analytics:
Predictive Maintenance: Multidimensional anomaly in vibration, current, temperature → RUL prediction (Remaining Useful Life). LSTM-Autoencoder, Isolation Forest. Accuracy: MAE < 10% on typical datasets.
Process Optimization: RL or Bayesian Optimization for process parameter optimization. Application: reactors, furnaces, compressors.
Quality Prediction: Online prediction of product quality by process parameters without waiting for lab analysis.
MES/ERP Integration
AI analysis results feed into MES (Manufacturing Execution System) and ERP for maintenance scheduling, parts ordering.
Cybersecurity for IIoT
OT/IT convergence — new attack surface. Network segmentation. Network-level anomaly detection (Claroty, Nozomi Networks).
Pipeline: 12–24 weeks
Depends on number of data sources, equipment types, and AI task complexity.