Industrial Data Historian for Manufacturing

What is an Industrial Data Historian?

An Industrial Data Historian (process data historian) is a specialized time-series database for high-frequency industrial and process data:

• Machine signals (status, counters, alarms)
• Process values (pressure, temperature, flow, torque, position)
• Quality and inspection data in time-series form

It is optimized for:

• Very many measurement points (tags)
• High write frequencies
• Long retention periods (years)
• Fast time-axis queries (trends, comparisons, aggregations)

In short: The historian stores "everything that changes in the process over time"—with minimal loss, compressed, and performantly queryable.

Core Functions of a Process Data Historian

Typical functions:

High-Frequency Data Capture: Connection of PLCs, DCS, SCADA, field devices, OPC UA, MQTT, IIoT gateways

Time-Series Storage & Compression: Optimized algorithms for historization, compression, and interpolation of time series

Query & Visualization: Trend curves, overlays, scatter plots, time window comparisons, export to BI/analytics tools

Context Enrichment: Linking time series with metadata (tag description, unit, control limits, plant structure)

Data Governance & Security: User rights, audit trails, redundancy, replication, long-term archiving

Difference from SCADA, MES, and Data Lake

SCADA: Visualizes and controls plants in real-time, typically stores limited history. → The historian is the long-term memory behind SCADA.

MES: Focuses on orders, OEE, quality, workflows, traceability. → MES uses process data but doesn't necessarily store it in full granularity. A historian is often the raw data source for MES analyses and PAT/predictive use cases.

Data Lake / Cloud Storage: Generic storage for various data types (documents, logs, sensor data). → A historian is specialized in industrial time series, often with interfaces to the data lake (export/replication).

Typical Use Cases

An Industrial Data Historian is central for:

Process Data Analysis & Troubleshooting: "What exactly happened on the night from 02:13 to 02:17?"—Zoom into pressure, temperature, torque, load, and failure progressions

OEE & Performance Optimization: Derivation of performance, downtime, and speed profiles based on time series

PAT & Predictive Quality: Foundation for multivariate models (MVDA), drift detection, and pattern analysis between process values and quality

Predictive Maintenance: Analysis of load, vibration, and temperature progressions to derive wear and failure probabilities

Energy Monitoring: Historization of energy, utilities, and consumption data as foundation for efficiency programs and compliance reporting

Role in Digital Shop Floor & IIoT Architectures

In modern digital factory/IIoT architectures, the Industrial Data Historian typically sits:

• Below or beside MES/Cloud MES
• Above SCADA/automation
• Connected to analytics, BI, and data science platforms

Typical architecture:

1. OT Layer: PLCs, DCS, field devices, OPC UA, gateways
2. Historian: Collects and stores process data as time series
3. MES / Cloud MES: Uses aggregated data for OEE, digital shop floor, quality, maintenance
4. Analytics / Data Lake: Uses historian data for advanced analytics, AI, reporting

This makes the Industrial Data Historian/Process Data Historian the technical foundation for all data-driven use cases in manufacturing—from OEE dashboards through PAT to predictive algorithms in smart factory contexts.