Historian Database Implementation

Understanding Historian Database Systems in Industrial Automation

In today's data-driven industrial landscape, the ability to collect, store, and analyse operational data has become essential for manufacturing facilities, utilities, and process industries across Atlantic Canada. Historian database implementation represents one of the most impactful automation investments that facilities can make, providing the foundation for operational excellence, regulatory compliance, and continuous improvement initiatives.

A historian database, also known as a process historian or data historian, is a specialized software application designed to collect and store time-series data from industrial processes. Unlike traditional relational databases, historian systems are optimised for high-speed data acquisition, efficient compression, and rapid retrieval of chronological process information. For Maritime industries ranging from seafood processing plants to pulp and paper mills, these systems have become indispensable tools for maintaining competitive advantage.

At Sangster Engineering Ltd., we have implemented historian solutions across diverse industrial sectors throughout Nova Scotia and the broader Atlantic region. This comprehensive guide explores the technical considerations, implementation strategies, and best practices that ensure successful historian database deployments.

Key Components of a Historian Database Architecture

A well-designed historian system comprises several interconnected components that work together to deliver reliable data collection and analysis capabilities. Understanding these components is crucial for planning an implementation that meets both current requirements and future growth expectations.

Data Collection Infrastructure

The foundation of any historian implementation is the data collection layer, which interfaces directly with plant floor systems. This typically includes:

• OPC (Open Platform Communications) servers that provide standardised connectivity to PLCs, DCS systems, and other automation controllers

• Native protocol interfaces for direct communication with specific equipment manufacturers such as Allen-Bradley, Siemens, and Schneider Electric

• MQTT brokers for IoT device integration and remote data collection from distributed assets

• Manual data entry interfaces for laboratory results, quality measurements, and operator observations

For facilities in Atlantic Canada, where operations may span multiple geographic locations—such as fish processing plants with satellite facilities along the Nova Scotia coastline—robust data collection infrastructure must account for network latency, intermittent connectivity, and store-and-forward capabilities.

Data Storage and Compression

Modern historian databases employ sophisticated compression algorithms that can achieve compression ratios of 10:1 to 40:1 while maintaining data fidelity. Common compression techniques include:

• Swinging door compression that captures data points only when values deviate beyond a specified threshold

• Deadband filtering that eliminates redundant readings within defined tolerance bands

• Lossy and lossless compression options depending on data criticality requirements

A typical medium-sized manufacturing facility collecting 5,000 data points at one-second intervals generates approximately 432 million data samples daily. Without compression, this would require several terabytes of storage annually. Effective compression reduces this to manageable levels while preserving the analytical value of the data.

Retrieval and Visualisation Tools

The value of collected data is realised through retrieval and visualisation capabilities. Modern historian platforms offer multiple access methods including trend displays, report generators, and API interfaces for integration with business intelligence systems. These tools enable operators, engineers, and managers to transform raw process data into actionable insights.

Planning Your Historian Implementation Project

Successful historian implementations require careful planning that addresses both technical and organisational factors. The planning phase typically represents 15-20% of the total project effort but significantly influences the ultimate success of the deployment.

Defining Data Collection Requirements

The first step in planning involves identifying which data points to collect and at what frequency. This requires collaboration between operations, engineering, and management stakeholders to understand:

• Regulatory compliance requirements under Canadian environmental and safety regulations

• Process optimisation objectives that require detailed operational data

• Equipment reliability and maintenance monitoring needs

• Quality assurance and traceability requirements for product certification

• Energy management and sustainability reporting obligations

For Nova Scotia facilities subject to provincial environmental regulations, historian data often serves as the official record for emissions monitoring, effluent quality, and resource consumption reporting. These compliance applications demand high data availability and integrity guarantees.

Infrastructure Assessment and Sizing

Proper system sizing ensures adequate performance throughout the expected system lifecycle. Key sizing parameters include:

• Tag count: The total number of data points to be collected, typically ranging from 500 tags for small facilities to 50,000+ for large industrial complexes

• Collection frequency: Scan rates ranging from milliseconds for high-speed processes to minutes for slowly changing environmental variables

• Retention period: Historical data storage requirements, which may span from months to decades depending on regulatory and operational needs

• Concurrent user load: The number of simultaneous users accessing trends, reports, and analytical tools

Server hardware specifications should accommodate projected growth of 15-25% annually in tag count and storage requirements. Many Atlantic Canadian facilities are experiencing increased data collection demands as they integrate more sensors, IoT devices, and predictive maintenance systems.

Network Architecture Considerations

Historian systems must operate reliably across industrial networks while maintaining cybersecurity best practices. Network design considerations include:

• DMZ (Demilitarised Zone) deployment that separates plant floor networks from business networks

• Redundant communication paths to ensure data collection continuity during network disruptions

• Bandwidth allocation that prevents historian traffic from impacting critical control system communications

• Remote access provisions for Maritime facilities requiring monitoring from centralised operations centres

Implementation Best Practices and Technical Standards

Drawing from extensive experience implementing historian systems across Atlantic Canada, Sangster Engineering Ltd. has developed proven methodologies that maximise project success rates and minimise implementation risks.

Phased Deployment Approach

Rather than attempting to collect all desired data points simultaneously, a phased approach allows teams to validate configuration, train users, and refine processes incrementally. A typical three-phase deployment might include:

Phase 1 (Weeks 1-4): Core infrastructure deployment and collection of critical process variables—typically 20-30% of total tag count representing key production parameters, safety interlocks, and regulatory measurements.

Phase 2 (Weeks 5-8): Expansion to include equipment monitoring, energy consumption, and quality metrics—bringing total collection to 60-70% of target tag count.

Phase 3 (Weeks 9-12): Final expansion incorporating auxiliary systems, environmental monitoring, and advanced analytics integration to complete the implementation scope.

Tag Naming Conventions and Data Organisation

Establishing consistent tag naming conventions during implementation prevents confusion and simplifies long-term system maintenance. Recommended practices include:

• Hierarchical naming structures that reflect physical plant organisation (Site.Area.Unit.Equipment.Parameter)

• Standardised abbreviations based on ISA-5.1 instrumentation symbols and identification standards

• Engineering units documentation using SI units consistent with Canadian industrial practice

• Metadata tagging that enables filtering and grouping by equipment type, process area, or data classification

For multi-site operations common in Atlantic Canada's resource industries, consistent naming conventions across facilities enable meaningful corporate-level analysis and benchmarking.

Data Quality Assurance

Historian data quality directly impacts the reliability of decisions based on that data. Implementation should include provisions for:

• Signal validation that flags out-of-range values, sensor failures, and communication errors

• Timestamping accuracy through NTP (Network Time Protocol) synchronisation across all data sources

• Audit trails that document any manual data modifications or system configuration changes

• Regular calibration verification correlating historian values with field instrument readings

Integration with Business Systems and Advanced Analytics

The full value of historian data is realised when it flows seamlessly into business systems and advanced analytical platforms. Modern historian implementations should be designed with integration capabilities as a core requirement.

Enterprise Resource Planning (ERP) Integration

Connecting historian data with ERP systems such as SAP or Microsoft Dynamics enables automated production reporting, inventory tracking, and cost allocation. Common integration scenarios include:

• Production batch records automatically populated with process parameters for quality traceability

• Energy cost allocation based on actual consumption measurements by production line or product

• Equipment runtime tracking for maintenance scheduling and asset depreciation calculations

• Raw material consumption reconciliation against theoretical usage rates

Predictive Analytics and Machine Learning

Historian databases provide the training data essential for predictive maintenance and process optimisation algorithms. Facilities collecting several years of operational data can leverage machine learning techniques to:

• Predict equipment failures days or weeks before they occur, enabling proactive maintenance scheduling

• Optimise energy consumption by identifying correlations between operating conditions and utility costs

• Improve product quality by analysing relationships between process parameters and quality outcomes

• Reduce environmental impact through optimised process control based on historical performance patterns

For Maritime industries facing competitive pressures and sustainability mandates, these analytical capabilities represent significant opportunities for operational improvement and cost reduction.

Cybersecurity and Data Protection Considerations

As historian systems collect sensitive operational data and interface with critical control systems, cybersecurity must be addressed throughout the implementation process. Canadian facilities must also consider privacy legislation and data sovereignty requirements.

Security Architecture Recommendations

Following frameworks such as IEC 62443 for industrial cybersecurity, historian implementations should incorporate:

• Network segmentation using industrial firewalls and VLANs to isolate historian components

• Role-based access control limiting data access and system administration privileges to authorised personnel

• Encrypted communications for data transmission between collection agents, servers, and client applications

• Regular security patching with tested updates applied during planned maintenance windows

• Intrusion detection monitoring to identify potential security threats or anomalous system behaviour

Backup and Disaster Recovery

Historian data often represents irreplaceable operational records. Comprehensive backup strategies should include:

• Automated daily backups with verification testing to confirm data recoverability

• Offsite backup storage protecting against site-level disasters—particularly important for coastal Nova Scotia facilities exposed to hurricane and storm surge risks

• Documented recovery procedures with defined recovery time objectives (RTO) and recovery point objectives (RPO)

• Redundant server configurations for critical applications requiring high availability

Measuring Success: Key Performance Indicators for Historian Systems

Evaluating historian implementation success requires both technical performance metrics and business outcome measures. Recommended KPIs include:

• Data availability: Percentage of expected data points successfully collected, with targets typically exceeding 99.5%

• System uptime: Historian server availability, targeting 99.9% or higher for critical applications

• Query response time: Average retrieval time for trend displays and reports, typically under 2 seconds for standard queries

• User adoption: Number of active users and frequency of system access across the organisation

• Business value realisation: Documented cost savings, efficiency improvements, or compliance achievements enabled by historian data

Regular review of these metrics enables continuous improvement and justifies ongoing investment in historian capabilities.

Partner with Sangster Engineering Ltd. for Your Historian Implementation

Implementing a historian database system is a significant undertaking that delivers lasting value when executed properly. From initial planning through deployment and ongoing optimisation, the technical decisions made during implementation shape the system's effectiveness for years to come.

Sangster Engineering Ltd. brings deep expertise in industrial automation and data systems to historian implementation projects throughout Nova Scotia and Atlantic Canada. Our team understands the unique requirements of Maritime industries and the technical challenges of operating in our regional environment.

Whether you are planning your first historian deployment, upgrading an existing system, or seeking to extract greater value from your operational data, we invite you to contact Sangster Engineering Ltd. to discuss your requirements. Our Amherst-based team is ready to help you transform your operational data into a strategic asset that drives efficiency, compliance, and continuous improvement across your organisation.

Partner with Sangster Engineering

At Sangster Engineering Ltd. in Amherst, Nova Scotia, we bring decades of engineering experience to every project. Serving clients across Atlantic Canada and beyond.

Contact us today to discuss your engineering needs.