Honeywell Forge & IoT

Honeywell Forge is a specialized software solution designed for verticals such as Building Management, and Industrial applications. This technical powerhouse serves as a unified platform, using IoT technologies to enhance operational efficiency, optimize resource utilization, and ensure a secure and connected environment within these specific verticals.  

Technical Verticals within Honeywell Forge 

Honeywell Forge provides tailored capabilities to address the distinct requirements of its target verticals: 

Building IoT Sensor Integration 

This component focuses on the technical integration of diverse sensors and devices within building infrastructures. The primary objective is to enable data acquisition for data-driven insights related to building operation and management. Technical aspects include: 

• Protocol Agnostic Connectivity: Support for various communication protocols commonly found in Building Management Systems (BMS), such as BACnet, Modbus, OPC UA, and proprietary protocols. This allows Honeywell Forge to ingest data from legacy and modern building equipment. 

• Sensor Data Normalization: Raw data from disparate sensors (e.g., temperature, humidity, occupancy, energy consumption meters) is ingested and transformed into a unified data model. This normalization is critical for consistent data interpretation and analysis across different building systems. 

• Edge Processing Capabilities: Deployment of edge gateways or controllers that perform local data aggregation, filtering, and pre-processing close to the data source. This reduces data latency, optimizes network bandwidth utilization, and can enable localized control loops for critical building functions. 

• Secure Data Ingress: Implementation of secure communication channels (e.g., TLS 1.2/1.3) for transmitting sensor data from edge devices to the Honeywell Forge cloud platform, ensuring data confidentiality and integrity. 

Industrial IoT Control System 

For industrial environments, Honeywell Forge incorporates a technical control system designed to monitor, analyze, and optimize industrial processes using IoT data. Key technical features include: 

• Operational Technology (OT) Integration: Direct interfaces and drivers for integrating with Industrial Control Systems (ICS), Distributed Control Systems (DCS), Supervisory Control and Data Acquisition (SCADA) systems, and Programmable Logic Controllers (PLCs). This allows for data acquisition from existing plant assets and control loops. 

• Real-time Process Data Acquisition: Capabilities for high-fidelity, high-volume ingestion of time-series data from industrial sensors, actuators, and control system tags. This data provides a current state representation of industrial processes. 

• Event and Alarm Management: Mechanisms for capturing, correlating, and prioritizing events and alarms generated by industrial equipment, providing operators with contextualized alerts. 

• Process Optimization Algorithms: Integration of advanced control algorithms and optimization routines that leverage real-time and historical IoT data to improve process stability, throughput, and resource efficiency. 

• Cybersecurity for OT: Embedded security features designed to protect industrial control systems from cyber threats, including network segmentation, anomaly detection within OT protocols, and secure remote access. 

Technical Constituents of Honeywell Forge IoT 

The Honeywell Forge platform is architected with several core technical constituents that enable its functionalities across verticals: 

IoT-Driven Data Access Layer 

This layer provides unified and secure access to operational data from various sources. 

• Data Ingestion and Aggregation: Supports ingestion from diverse sources, including industrial protocols (e.g., OPC UA, Modbus TCP), building protocols (e.g., BACnet IP), and standard IoT protocols (e.g., MQTT, HTTP/REST). Data is aggregated from edge devices and direct cloud connections. 

• Unified Data Model: Raw ingested data is mapped to a canonical data model. This abstraction layer provides a consistent representation of assets, processes, and entities, regardless of their source protocol or vendor. This enables consistent analytics and application development. 

• Data Contextualization: Raw sensor and control data is enriched with contextual metadata, such as asset hierarchies, geographical locations, operational states, and maintenance histories. This provides semantic meaning to the data, facilitating more accurate analysis. 

• Secure Data Interoperability: Implements APIs and interfaces (e.g., GraphQL, RESTful APIs) for authorized applications and services to securely access processed and contextualized data. Access control is managed through identity and access management (IAM) systems. 

Analytics and Machine Learning Engine 

This component provides the computational capabilities for processing and deriving insights from the ingested data. 

• Time-Series Data Processing: Optimized for high-volume time-series data, enabling real-time calculations, aggregations, and statistical analysis. 

• Predictive and Prescriptive Analytics: Utilizes machine learning algorithms for tasks such as anomaly detection, predictive maintenance (forecasting equipment failure), and prescriptive recommendations for operational adjustments. Models are often trained on historical operational data. 

• Digital Twin Capabilities: Constructs digital representations (digital twins) of physical assets, processes, or entire environments. These digital twins leverage real-time IoT data and historical information to simulate behavior, predict performance, and enable "what-if" scenario analysis. 

• Domain-Specific Algorithms: Includes pre-built analytical models and algorithms tailored to specific industrial or building management use cases, leveraging Honeywell's domain expertise. 

Real-time Visibility and Control Dashboard 

This constituent provides a user interface for monitoring and interacting with the connected environment. 

• Customizable Data Visualization: Enables the creation of dashboards with graphical representations (e.g., trend charts, KPIs, heat maps) of real-time and historical operational data. 

• Hierarchical Views: Supports hierarchical organization of assets and locations, allowing users to drill down from a high-level portfolio view to individual device details. 

• Alarm and Event Management: Displays real-time alarms and events from connected systems, often with contextual information and prioritization, facilitating rapid response. 

• Remote Control Interface: Provides secure interfaces for issuing commands and adjusting parameters on connected devices or control systems, subject to role-based access controls. 

Extensible Application Framework 

This component provides the tools and environment for developing and integrating custom applications and solutions on top of the Honeywell Forge platform. 

• APIs and SDKs: Offers a suite of APIs (e.g., RESTful, GraphQL) and Software Development Kits (SDKs) that allow third-party developers or internal teams to build custom applications, integrations, or data connectors. 

• Microservices Architecture: The underlying platform likely utilizes a microservices architecture, promoting modularity, scalability, and independent deployment of services. 

• Containerization Support: Potentially supports containerization technologies (e.g., Docker, Kubernetes) for deploying and managing applications within the platform environment, ensuring portability and scalability. 

• Data Lakes and Data Warehouses: Provides interfaces to underlying data storage solutions (e.g., data lakes for raw data, data warehouses for structured analytics) for advanced data science and reporting. 

Learn more https://www.honeywellforge.ai/