⚙️ Machine Data Monitoring + Predictive Maintenance in Pharma

In pharma manufacturing, minor deviations in temperature, vibration, or pressure can trigger batch rejections, CAPAs, or even regulatory citations. Yet machine intelligence is often missing from compliance pipelines.

🧩 The Problem

Despite SCADA and PLC systems, most insights arrive after a fault. Maintenance is reactive, quality focuses on output — not equipment. A lack of visibility leads to undetected risks and unplanned downtime.

⚡ The Vision

This PoC aims to ingest live machine data (via KepServer + OPC UA), analyze critical tags (temperature, pressure, RPM), predict threshold breaches, and quantify machine performance before and after maintenance.

🏗️ Architecture Overview

1. Data Ingestion

• Used OPC UA protocol to extract live tag values
• Sensor types: temperature, RPM, motor currents, counters
• Data stored in InfluxDB or PostgreSQL

2. Time Series + Anomaly Detection

• Rolling means, Z-scores, Isolation Forest for flagging deviations
• Grouped into Critical Process Parameters (CPPs)
• Events logged for audit + traceability

3. Forecasting Threshold Breaches

• Forecast using Prophet, ARIMA, or LSTM
• Predict next 30–60 minutes of process behavior
• Trigger alerts for projected control limit violations

4. Maintenance Intelligence

• Calculate uptime %, OEE, MTBF
• Compare metrics pre/post maintenance
• Justify maintenance spend via analytics

5. Visualization Dashboard

• Live sensor plots with anomaly overlays
• Compliance scorecards per batch or line
• Drilldowns on deviation trends and machine behavior

💡 Pharma Impact

• Reduces CAPAs by catching issues early
• Improves GMP compliance and traceability
• Enables cost-effective, just-in-time maintenance
• Drives Pharma 4.0 transformation at plant level

🧠 Future Additions

• Golden batch profiling for deviation scoring
• Root cause linking for known tag patterns
• MES/ERP integration for auto-generated work orders
• Edge AI deployment for shop-floor intelligence

📎 Final Thoughts

By combining OPC UA connectivity with anomaly detection and forecasting, this PoC bridges the gap between machine behavior and compliance impact. The potential for cost savings and regulatory readiness is massive.

For now, it's a proof-of-concept — but it's built with production in mind.