Aleph FlexServer

Standardize Legacy Machines
Future-Proof Your Factory

Aleph FlexServer transforms any legacy equipment into an OPC UA-compliant data hub, bridging the gap between old systems and modern AI/ML solutions

Universal Legacy Connectivity

Custom Data Connectors: We develop adapters for any unsupported legacy machine—proprietary protocols, PLCs, or OEM-specific systems (e.g., Hauni VHK, GD aHMI).

Industry-Standard Support: Pre-built connectors for Siemens S7, Beckhoff ADS, Modbus, and more.

OPC UA Powerhouse

Companion Specs Ready: Exposes data using industry standards like TMC (OPC 30060) or other OPC UA information models.

Semantic Precision: Full implementation of OPC UA variables, methods, and events—not just data mapping.

Future-Ready: Enables integration with Aleph Visual, Aleph Insights AI, or any OPC UA client.

Scalable and Secure

Docker-Friendly: Deploy on-premises, edge devices, or cloud.

Enterprise-Grade Security: Role-based access, encrypted M2M communication, and OPC UA Pub/Sub support.

Scalability: Manages thousands of nodes across brownfield and greenfield environments.

Zero-Downtime Integration

Works with Live Systems: Map data without disrupting production.

ISA-95/88 Compliance: Aligns with enterprise manufacturing hierarchies.

Why Aleph FlexServer?

No Machine Left Behind

Most OPC UA servers ignore legacy protocols. FlexServer starts there, turning “unconnectable” machines into smart assets.

Built for Real-World Complexity

Automates workflows (e.g., parses production orders, dispatches recipes per TMC specs) and applies data transformations to align unstructured signals with OPC UA models.

Proven in Global Factories

Trusted by Philip Morris International to unify multi-OEM, multi-generation production lines under the TMC standard.

Success Story Snapshot: Philip Morris International

Challenge

Diverse legacy machines from multiple OEMs lacked standardized connectivity.

Solution

Deployed Aleph FlexServer with custom TMC OPC UA models and connectors.

Result

Enabled Philip Morris International’s IIoT data stack and AI-driven Speed Management Controller.

Technical Specifications

We are proud to serve a wide range of industries, providing tailored IT solutions that address specific challenges and opportunities.

Platform

• Docker
• Edge controllers, on-prem servers

Supported Protocols

• OPC UA
• Siemens S7
• Beckhoff ADS
• Modbus
• Hauni VHK
• GD aHMI REST
• Custom develoed connectors

Standards

• OPC UA
• ISA-95/88
• TMC (OPC 30060).

Why Manufacturers Choose Aleph

Our clients' success is our top priority. Here's how Aleph has transformed their manufacturing with our technology solutions.

"We engaged with Aleph Digital Industry to create a robust, scalable, and adaptable edge solution to control machines exclusively through OPC UA."
"Aleph authored a rich OPC UA information model driving interoperability in the tobacco industry."
"Aleph Digital Industry brought new life to a very old machine!"
Milestones

Aleph's ride

2024

Aleph Visual is released on the App Store

Aleph Visual is a zero-touch, monitoring and control App that visualizes the OPC UA data exposed by a machine. The user experience is focused on situation awareness to support operators work on what is key.

Aleph Visual provides several benefits:

  1. Intuitive Monitoring & Control Interface
    • Streamlined, user-friendly design tailored for busy shop-floor personnel.
    • Enhances situational awareness with incremental data display and contextual insights.
  2. OPC UA Integration
    • Connects seamlessly to machines and processes via OPC UA for real-time data access.
    • Supports brownfield plants, modernizing legacy systems into Industry 4.0-ready infrastructure.
  3. Zero-Engineering Visualization (Canvas)
    • Graphical Canvas provides a hierarchical overview of machines and processes.
    • Retrieves graphical resources directly from OPC UA servers for instant visualization.
    • Uses standardized UI elements for machines lacking graphical resources, ensuring consistent look-and-feel across OEMs.
    • Customizable to meet corporate branding and operational requirements.
  4. Dynamic Data Inspection (Inspector)
    • Displays OPC UA object data side-by-side with the Canvas for contextual analysis.
    • Enables editing of properties/variables (where server permissions allow).
    • Customizable views: hide, pin, or prioritize critical data points.
    • Direct integration with Trend View for monitoring selected variables over time.
  5. Advanced Trend Analysis (Trend View)
    • Historical & Live Data: View up to 8 variables in historical windows or live updates (last-minute mode).
    • Aggregates Computation: Automatically calculates max, min, and average values for selected timeframes.
    • Compact Historization: Stores data locally on small controllers for efficient, low-footprint archiving.
  6. Legacy Machine Modernization
    • Extends the usable lifespan of older machines by adding modern OPC UA interfaces.
    • Minimal engineering effort required for deployment, even on outdated equipment.
  7. OPC UA Expertise & Standards Compliance
    • Corporate member of the OPC Foundation; contributor to OPC 30060 Companion Specification (Tobacco Machine Communication).
    • Ensures secure, standardized connectivity across diverse machinery and systems.
  8. Customization & Scalability
    • UI elements and workflows adaptable to corporate-specific needs.
    • Scalable for global rollouts, proven by partnerships with multinational manufacturers.
  9. Proven Industry Impact
    • Successfully deployed at Continental, improving usability and extending machine lifespan.
    • Client Testimonial: “Brought new life to a very old machine on our shop floor.”

CTA: Read the Success Story

2024

Aleph Insights includes ALIF Anomaly Detection

ALIF stands for Active-Learning Isolation Forest, it is anomaly detection done right for hard tasks: starting with no labels, it takes maximum benefit from feedback labels as the expert provides overruling incorrect anomaly identification.

Aleph Insights provides several benefits compared to other Ml techniques:

  1. No Initial Labels Required
    • Operates without initial labels, improving incrementally as feedback is provided.
    • Critical for scenarios where anomalies are rare or costly to label.
  2. Feedback-Driven Improvement
    • Incorporates expert feedback by prioritizing the most informative instances (e.g., “most anomalous” or “maximum uncertainty”).
    • Accelerates model accuracy with reduced labeling effort.
  3. Lightweight & Computationally Efficient
    • Avoids full retraining; updates only leaf depths in the Isolation Forest structure for rapid adjustments.
    • Designed for Edge implementations, ensuring scalability and low resource consumption.
  4. Superior Detection Accuracy
    • Outperforms baseline methods like Isolation Forest-AAD (IF-AAD) and Random Forest.
    • Achieves higher accuracy with fewer labeled samples compared to traditional approaches.
  5. User-Centric Anomaly Detection
    • Combines active learning with the Isolation Forest framework, bridging unsupervised detection and supervised precision.
    • Tailored for real-world manufacturing applications requiring adaptable, efficient solutions.

For engineers: (CTA) Read the Science https://doi.org/10.48550/arXiv.2207.03934

For managers: (CTA) Book a Consultation

2023

The Speed Management Controller (SMC) solution, based on Aleph Insights, is rolled out globally

The Speed Management Controller (SMC) improves the throughput and the MTBF (mean time between failure) of discrete production lines. Like adaptive cruise control in automobiles, SMC ensures machines operate at an optimal pace, slowing down when material accumulates in downstream buffers and speeding up when buffers are running low. SMC uses Aleph Insights ML to learn from past unplanned stops and adjusts speed accordingly for future production cycles.  

The implementation of SMC was performed at Philip Morris International on legacy machines leading to substantial improvements and prompting a global roll out.

SMC provides several benefits that make it a unique manufacturing AI solution:

  1. Edge-Based Machine Learning
    • Runs on low-cost Edge devices (e.g., Raspberry Pi) without requiring cloud connectivity.
    • Connects directly to machines via OPC UA for real-time data access.
  2. Learn-and-Control Capability
    • Unique in the industry: Learns from machines and controls them to optimize performance (e.g., speed, quality).
    • Continuously adapts to changing conditions like wear-and-tear or production shifts.
  3. Causal Model Technology
    • Uses explainable causal models to identify cause-effect relationships in data.
    • Enables transfer learning, accelerating deployment across similar machines.
  4. No Offline Training Required
    • Eliminates computationally expensive offline training phases.
    • Models update in real-time during operation.
  5. Legacy Machine Integration
    • Works with both modern and legacy machines via OPC UA servers.
    • Transforms brownfield plants into Industry 4.0-ready facilities.
  6. Full-Stack AI Solution
    • Includes OPC UA servers, causal model visualization, and professional services for model design.
    • Future plans to enable third-party causal model development.
  7. TMC OPC UA Compliance
    • Based on the OPC 30060 Companion Specification for tobacco machinery for faster roll out.
    • Ensures secure, standardized connectivity across OEMs and systems.

For engineers: At the SPS 2024, (CTA) Watch the Video

For managers: (CTA) Read the Success Story

2022

The Adaptive Advanced Control (AAC) solution, based on Aleph Insights, is rolled out globally

The Adaptive Advanced Controller (AAC) is a micro-service that runs in an Edge device close to the machine to predict and control the process tracking the required setpoint. AAC learns how the process inputs cause a change in the output, further improving the process with other external variables, i.e. regressors. AAC is based on Aleph Insights, our AI solution designed for the manufacturing shop floor.

AAC was used at Philip Morris International for two different use cases targeting different quality target variables on legacy machines leading to substantial improvements and prompting a global roll out.

AAC provides several benefits that make it a unique manufacturing AI solution:

  1. Edge-Based Machine Learning
    • Runs on low-cost Edge devices (e.g., Raspberry Pi) without requiring cloud connectivity.
    • Connects directly to machines via OPC UA for real-time data access.
  2. Learn-and-Control Capability
    • Unique in the industry: Learns from machines and controls them to optimize performance (e.g., speed, quality).
    • Continuously adapts to changing conditions like wear-and-tear or production shifts.
  3. Causal Model Technology
    • Uses explainable causal models to identify cause-effect relationships in data.
    • Enables transfer learning, accelerating deployment across similar machines.
  4. No Offline Training Required
    • Eliminates computationally expensive offline training phases.
    • Models update in real-time during operation.
  5. Legacy Machine Integration
    • Works with both modern and legacy machines via OPC UA servers.
    • Transforms brownfield plants into Industry 4.0-ready facilities.
  6. Full-Stack AI Solution
    • Includes OPC UA servers, causal model visualization, and professional services for model design.
    • Future plans to enable third-party causal model development.

Do you have similar needs? (CTA) Schedule a Consultation

2021

Aleph releases Aleph Insights, causal ML that runs at the machine

Aleph releases Aleph Insights, an adaptive, real-time AI/ML and control engine based on causal principles and designed for manufacturing processes. Aleph Insights is available as a Docker container exposing an OPC UA interface and as a library to include in applications.

Several features set Aleph Insights apart:

  1. Edge-Based Machine Learning
    • Runs on low-cost Edge devices (e.g., Raspberry Pi) without requiring cloud connectivity.
    • Connects directly to machines via OPC UA for real-time data access.
  2. Learn-and-Control Capability
    • Unique in the industry: Learns from machines and controls them to optimize performance (e.g., speed, quality).
    • Continuously adapts to changing conditions like wear-and-tear or production shifts.
  3. Causal Model Technology
    • Uses explainable causal models to identify cause-effect relationships in data.
    • Enables transfer learning, accelerating deployment across similar machines.
  4. No Offline Training Required
    • Eliminates computationally expensive offline training phases.
    • Models update in real-time during operation.
  5. Legacy Machine Integration
    • Works with both modern and legacy machines via OPC UA servers.
    • Transforms brownfield plants into Industry 4.0-ready facilities.
  6. Full-Stack AI Solution
    • Includes OPC UA servers, causal model visualization, and professional services for model design.
    • Future plans to enable third-party causal model development.

(CTA) Book a Demo

2023

Aleph releases a Production Order Orchestration Layer (POOL) micro-service for Continuous Processes

POOL provides a standardized framework for executing production orders across any ISA-88-compliant Process Cell.

Leveraging on OPC UA, POOL gives end users the freedom to integrate best-of-breed equipment and controls' technology.

The implementation of POOL was performed at Philip Morris International on legacy machines leading to substantial improvements and prompting a global roll out.

POOL features follow:

1. Production Order Management
  • Release/Unrelease Production Orders: Ability to release or unrelease production orders (POs) to/from the Process Cell, a.k.a. Production Line.
  • Assign/Unassign Production Orders: Assign POs to specific Units or unassign them when no longer needed.
  • Start/Stop Production Orders: Start or stop POs on specific Units, with support for manual and automatic triggers.
  • Complete Production Orders: Mark POs as complete when production is finished, with automatic completion based on predefined conditions.
  • Abort Production Orders: Abort POs in case of errors or interruptions, transitioning the state machine to an aborted state.
  • Get Production Order Information: Retrieve detailed information about POs, including material lists, datasets, and production status.
2. State Machine Management
  • Production Order State Machine: Manage the lifecycle of POs through states such as Released, Assigned, Started, Executing, Completing, Aborted, and Stopped.
  • Unit State Machine: Manage the state of individual Units through states such as Assigned, Executing, Completing, Aborted, and Stopped.
  • Automatic State Transitions: Support for automatic transitions between states based on conditions such as AutoStart and AutoComplete settings.
  • Manual State Transitions: Allow manual intervention to transition states (e.g., starting, stopping, or aborting POs).
3. Automation Features
  • AutoStart: Automatically start production on a Unit when upstream conditions are met (e.g., when the upstream Unit is running the same PO).
  • AutoComplete: Automatically complete production on a Unit when downstream conditions are met (e.g., when the downstream Unit is complete or has a different PO).
  • AutoStop: Automatically stop production on a Unit when conditions are met (e.g., when no more input materials are available).
4. Data Flow and Integration
  • OPC UA Compliance: POOL is OPC UA compliant, enabling seamless integration with OPC UA servers and clients.
  • Dynamic Machine Module Discovery: POOL dynamically discovers Units from the connected servers, reducing configuration effort.
  • Data Flow Orchestration: POOL orchestrates data flow between Units and higher level systems.
  • Non-OPC UA Payload Handling: Support for handling non-OPC UA payloads (e.g. MOM/MES payloads) alongside OPC UA payloads.
5. Material and Inventory Management
  • Material List Management: Retrieve and manage material lists associated with production orders.
  • Inventory Tracking: Track inventory of finished goods, semi-finished goods, and by-products.
  • Material Loading/Unloading Points: Manage material loading and unloading points for each Unit ensuring correct material flow.
6. Event Handling and Messaging
  • Event Generation: Generate events for material consumption, dispensing, and production status changes.
  • Message Publishing/Subscription: Publish and subscribe to messages via MQTT or other protocols for real-time communication.
  • Error Handling: Provide feedback and error handling for failed operations (e.g., missing resources, invalid states).
7. Central Visualization and Control
  • Central Visualization (CV) Integration: Expose PO states and variables to a central visualization system for monitoring and control.
  • Read-Only PO Variables: Expose read-only PO variables (e.g., status, assigned POs) via OPC UA for external systems.
  • Event Exposure: Expose events when POs change state (e.g., released, assigned, started, aborted).
8. Persistence and Recovery
  • PO Persistence: Persist production order data to rebuild the status in case of service downtime.
  • State Recovery: Recover the state of POs and machine modules after a service restart or failure.
9. Multi-PO Handling
  • Continuous Production: Support for continuous production mode, allowing interleaving of POs across multiple machine modules without interruption.
  • Interleaving POs: Support for interleaving POs in continuous production modes, allowing seamless transitions between different POs.
10. Micro-Service Architecture
  • Stateless Design: POOL is designed as a stateless micro-service, making it robust and less prone to getting stuck in corner cases.
  • MQTT Integration: Subscribe to MQTT topics for receiving POs and publish events for logging and monitoring.
11. Diagnostic and Feedback
  • Execution Feedback: Provide detailed feedback on the execution of commands (e.g., success, failure, error codes).
  • State Machine Diagnostics: Expose state machine transitions and conditions for diagnostic purposes, especially when AutoStart or AutoStop is enabled.
12. Compliance and Standards
  • TMC Compliance: POOL is compliant with TMC standards, ensuring interoperability with TMC units and systems.
  • ANSI/ISA-S88.01-1995 Compliance: Support for batch control standards, especially in handling WIP (Work in Progress) containers and storage units.
13. Customization and Configuration
  • Configurable Units: POOL can be configured to work with any number of Units, as defined in the Aggregation Server.
  • Recipe Parameter Mapping: Map recipe parameters from MOM/MES to TMC datasets, ensuring correct machine settings for each PO.

CTA: Consult about your needs

2023

Aleph releases the Aggregation Publish Subscribe (APS) micro-service, OPC UA - MQTT gateway and OPC UA aggregator

The Aggregation Publish Subscribe (APS) micro-service provides scalable, secure, and compliant aggregation of OPC UA servers, enabling unified data publishing/subscribing via MQTT ideal for data fabric. Its dynamic configuration, resource-efficient design, and integration make it suitable for industrial automation in regulated and non regulated environments.

APS is now available as a FlexServer component. The main features are listed:

1. Core Functionality
  • Multi-Server Aggregation:
    • Aggregates one or more underlying OPC UA servers (both TMC-compliant and non-TMC) into a single OPC UA server namespace.
    • Merges namespaces dynamically, handling static and dynamic namespaces with unique URIs to avoid conflicts.
    • Supports automatic discovery of servers via Local Discovery Server (LDS) or manual configuration.
  • MQTT Pub/Sub:
    • Publishes aggregated data to MQTT topics following Enterprise-specified topic structures (e.g., TMC\MachineModuleType\<Instance>\...).
    • Subscribes to MQTT topics to trigger methods or write variables in underlying servers.
    • Supports payload translation for method execution (e.g., mapping MQTT messages to OPC UA method arguments).
2. Dynamic Configuration & Scalability
  • Zero-Config:
    • Starts with a "last known good" AddressSpace (AS) configuration to minimize setup.
    • Automatically updates the exposed Aggregation Server (AS) if changes are detected (e.g., via GeneralModelChangeEvent or server version updates).
  • Lazy Browsing:
    • Performs background browsing of AddressSpace changes in low-priority threads to reduce resource load.
  • Update-as-You-Go:
    • Dynamically adjusts Writer/ReaderGroups and DataSet configurations when AS changes.
3. Security & Compliance
  • Secure Connections:
    • Uses highest available security protocols (e.g., Basic256 – Sign & Encrypt) for OPC UA and MQTT.
    • Supports TLS for MQTT broker communication.
    • Certificate management via local storage.
  • Access Control:
    • Excludes servers or topics via configuration rules (regular expressions).
4. Performance & Resource Management
  • Limited Buffering Policy:
    • Restricts message retention to a maximum of 1 hour (configurable) to prevent excessive resource usage.
  • Adaptive Threading:
    • Adjusts thread count based on network capacity and message load.
  • Efficient Reconnections:
    • Monitors server health and handles OPC UA reconnections with minimal disruption.
5. Integration & Interoperability
  • TMC Compliance:
    • Aggregates TMC-compliant servers while preserving namespace versioning and compliance.
    • Publishes TMC payloads to MQTT topics for integration with systems like POOL and MPS.
  • ISA 88/95 Alignment:
    • Supports ISA 88 batch control and ISA 95 enterprise-control integration models.
  • OPC UA Standards:
    • Complies with OPC UA PubSub (OPC 10000-14) for message encoding, transport, and security.
6. Configuration & Deployment
  • Environment Variables:
    • Configured via variables (e.g., PUBSUB_MQTT_HOST, AGG_PORT, PUBSUB_PUBLISHINGINTERVAL).
    • Supports rules for topic filtering, QoS settings, and publishing intervals.
  • Docker Containerization:
    • Deployed as a Docker container for edge computing.
7. Advanced Features
  • Event Handling:
    • Aggregates and forwards OPC UA events (e.g., alarms, state changes) to MQTT topics.
  • Method Execution:
    • Translates MQTT messages into OPC UA method calls with feedback (e.g., ExecutionFeedback).
  • Delta Frames & Keyframes:
    • Optimizes data transmission using delta encoding and periodic keyframes (configured via PUBSUB_KEYFRAMECOUNT).
8. Fault Tolerance & Monitoring
  • Retention Policies:
    • Drops outdated messages after a configurable period to maintain system performance.
  • Health Monitoring:
    • Tracks server status via OPC UA ServerStatus and BuildInfo variables.
    • Logs state transitions and errors for diagnostics.

2022

The OPC Foundation releases Version 2.0 of the Tobacco Machine Communication (TMC) OPC UA Companion Specification, OPC30060 authored by Aleph Digital Industry

The OPC Foundation releases for public use Version 2.0 of the OPC 30060 OPC UA Companion Specification for Tobacco Machine Communication (TMC) Joint Working Group.

Building on the strengths of version 1.0, version 2.0 extends the TMC OPC UA information modeling scope to the tobacco Primary Process including Kitchen Preparation, Secondary Make and Pack including Reduced-Risk Products (RRP) and Non-Tobacco Materials, NTMs.

OPC 30060 v2.0 introduces the following new interoperability features:

  1. Production Order Orchestration Layer:
    • Orchestration of Production Orders: coordinates production orders accross MachineModules in a Production line, a.k.a. Process Cell, compliant with ANSI/ISA-88.00.01-2010 Physical Model
    • Production Order Retention: automatically cleans up production orders when complete;
    • External Application Integration: acts as an external application interactig with Machine Modules but not directly implemented by them.
    • State Management: clearly exposes the state of production with ad hoc state machines
    • Interaction Logic: clearly defines how machines interact with one another to execute a production order. The logic is vendor and OEM independent.
    • Material Flow Control: specifies material sources and destinations for material routing during order execution.

2021

Aleph releases the FlexServer TMC OPC UA Server

Aleph releases FlexServer, the first TMC compliant OPC UA server including Historical Access (OPC10000-11) with  with connectors for legacy equipment (Hauni VHK, GD aHMI REST). Aleph FlexServer includes APS, AGS, PSM.

Aleph FlexServer features follow:

Core Features
  1. Native TMC2.0 OPC UA Server:
    • Fully configurable via local files for easy roll-out.
  2. Three-Layer Architecture:
    • Aleph FlexServer Core: The core TMC2.0 OPC UA Server application.
    • Aleph FlexServer Data Providers: Project-specific functions for executing methods and generating events.
    • Aleph FlexServer Configuration Connectors: Functional calls orchestrating the Core and Data Providers.
  3. Multiple Deployment Options:
    • Run as a Desktop Application.
    • Run as a Windows Service.
    • Run as a Docker Container.
  4. Data Connectors:
    • Supported Protocols:
      • Beckhoff ADS: Connects to Beckhoff systems supporting ADS protocol v2.1.1.
      • Siemens S7-Ethernet: Connects to Siemens CPU 200/300/400/1200/1500 series.
      • Hauni VHK: Connects to HAUNI machines featuring VisuHost protocol.
      • OPC XML-DA: Connects to systems exposing OPC XML-DA v1.01.
      • OPC UA: Connects to systems exposing OPC UA servers implementing DataAccess Server Facet.
      • Modbus TCP: Supports Modbus TCP protocol.
      • GD aHMI: Connects to GD aHMI systems.
    • Refresh Rate: Minimum 500ms.
    • Mapping Configuration: Schema-based JSON files for static and dynamic values, with support for formulas to expose conditioned values.
    • Maximum Connectors: Up to 8 connectors in total.
  5. Start-up Configuration:
    • Configurable via XML file or environment variables.
    • Supports settings for data storage, logging, OPC UA endpoints, security, and more.
  6. Users and Roles Management:
    • Supports Anonymous and User/Password authentication.
    • Configurable via XML files (FlexServerUserConfiguration.xml and FlexServerUserRoleConfiguration.xml).
  7. Data Providers:
    • NotSupportedDataProvider: Returns a BadNotSupported status code.
    • ValueDataProvider: Provides a static value.
    • InMemoryValueDataProvider: Stores values in memory (not retained after restart).
    • JsonFileValueDataProvider: Stores values in a JSON file (retained after restart).
    • NoSqlDataProvider: Stores values in a local NoSQL database (retained after restart).
    • DataSourceValueDataProvider: Provides values from a specified data source and address.
    • DataSourceExpressionDataProvider: Provides values based on expressions calculated from input addresses.
    • DataSourceTotalizerDataProvider: Computes the total of values from a specified address.
    • DataSourceRateDataProvider: Computes the rate of values from a specified address.
    • ParentObjectPropertyValueDataProvider: Provides values from parent object properties.
    • EngineeringUnitDataProvider: Provides EUInformation values based on unit of measure.
    • StopReasonListDataProvider: Provides StopReasonList values from a CSV file.
    • DataSetListDataProvider: Provides DataSetList values from a CSV file.
    • DatasetDataProvider: Provides DataSet values based on DataSetList definitions.
    • DataSourceValueThresholdSelectorDataProvider: Maps values to thresholds.
    • StructureValueDataProvider: Combines values from other DataProviders into a structured value.
  8. Method Implementation Providers:
    • Default implementations for TMC type methods.
    • Configurable via JSON files.
    • Supports MethodDataProviders for executing logic in underlying systems.
    • Methods can be configured to ignore the MachineModule.Remote setting.
  9. Event Providers:
    • DataSourceDowntimeEventProvider: Generates DowntimeLogType events based on state changes.
    • TriggerEventProvider: Abstract provider for triggering events based on boolean values.
    • IntegrityRejectedMaterialEventProvider: Generates events for rejected material.
    • LoadingPointUnloadedEventProvider: Generates events when a loading point is unloaded.
    • MaterialConsumedEventProvider: Generates events when material is consumed.
    • MaterialDispensedEventProvider: Generates events when material is dispensed.
    • MaterialUnloadingRequiredEventProvider: Generates events when material unloading is required.
    • NewPresentedMaterialEventProvider: Generates events when new material is presented.
  10. Default Logic for ObjectTypes:
    • MachineModuleConfigurationType: Generates events for changes in StopReasonList, RootCauseList, and RootCauseGroupList.
    • MachineModuleLiveStatusType: Generates events for state changes and populates alarms based on CSV files.
    • MachineModuleProductionType: Generates events for production status changes.
    • MachineModuleSetupType: Generates events for dataset changes and maps files to OPC UA documentation folders.
    • MaterialLoadingPointType: Generates events for material consumption and dispensing, and provides historization for key variables.
    • MaterialOutputPointType: Generates events for material production and provides historization for key variables.
    • MaterialRejectionPointType: Generates events for material rejection and provides historization for key variables.
    • MaterialStorageBufferType: Provides historization for loading and unloading rates.
    • DefectDetectionSensor: Generates events for detection mode changes and defect detection.
    • SensorFunction: Generates events for detection mode changes.
    • DefectReason: Generates events for detection mode changes and defect detection.
    • ProcessItem: Provides historization for process variables and computes aggregates (avg, std, max, min, total).
    • ProcessControllern: Provides historization for process control variables and implements logic for remote control.
    • ProcessControlLoop: Implements watchdog logic for process control loops.
Additional Features
  1. Historization:
    • Built-in historization for key variables across various object types (e.g., MaterialLoadingPointType, MaterialOutputPointType, ProcessItemType, etc.).
    • Supports retention periods for historical data.
  2. Aggregates Calculation:
    • Automatically computes aggregates (avg, std, max, min, total) for process variables.
  3. Remote Control:
    • Supports remote control of process variables through RemoteControl, RemoteValue, and RemoteControlEnable.
  4. Watchdog Logic:
    • Implements watchdog logic for process control loops as per TMC specifications.
  5. Documentation Mapping:
    • Automatically maps files in the DATASTORAGEPATH/Documentation_<MachineModuleName>folder to OPC UA documentation properties.
  6. Revision History:
    • Regular updates and improvements with detailed version history.
Supported Protocols and Standards
  • OPC UA: Fully compliant with OPC UA standards.
  • TMC2.0: Implements the Tobacco Machine Communication Information Model for OPC UA.
  • Industrial Protocols: Supports Beckhoff ADS, Siemens S7-Ethernet, Hauni VHK, OPC XML-DA, OPC UA, Modbus TCP, and GD aHMI.
System Requirements
  • Operating System: Linux (Docker Engine), Windows 10/11 (Docker Desktop), Windows 2022 Server (Docker Enterprise Edition).
  • CPU Architecture: ARM32, ARM64, x86, x64.
  • Hardware: Minimum 1GB RAM, 300MB storage, 1.5GHz CPU.
Licensing
  • Licensed per machine module in scope.
  • License Types:
    • Perpetual License: One-time purchase with optional annual support.
    • SaaS License: Annual subscription including support, upgrades, and updates.
MQTT Features
  • Supported Protocols: MQTT v3.1, v5.0.
  • Roles: Publisher and Subscriber.
  • Topic Configuration: OPC UA BrowserPath-based schema, JSON files, and regular expression rule-based overrides.
  • Buffering: Rolling 1-hour buffer for MQTT messages.
OPC UA Client Features
  • OPC UA Roles: OPC UA Client.
  • Specifications: OPC Unified Architecture V1.05.01, OPC 30060 Tobacco Machine Communication V2.00.1.
  • Profiles: Standard UA Client 2017 Profile, TMC OPC UA Client Profile.
  • Facets: State Machine Client, Durable Subscription, Data Access, Event Subscriber, Historical Access, Multi-Server Client Connection.
Host Requirements
  • Operating Systems: Linux, Windows 10/11, Windows 2022 Server.
  • CPU Architectures: ARM32, ARM64, x86, x64.
  • Hardware Requirements: Minimum 1GB RAM, 300MB storage, 1.5GHz CPU.
  • Logging: Built-in trace and audit logging, integrated with Docker.

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2020

Aleph releases the Publish/Subscribe Module (PSM) microservice, OPC UA - MQTT gateway

Aleph releases PSM, the first OPC UA - MQTT publisher based on OPC 10000-14 optimized for Data Fabric and Enterprise use.

PSM ensures compliance with OPC UA Part 14, enabling scalable, secure, and interoperable Pub/Sub communication for industrial IoT, cloud analytics, and real-time control systems.

PSM is now available as a component of the Aleph FlexServer.

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Several key features of PSM:

  1. DataSet Management
    • Support PublishedDataSets for cyclic (e.g., sensor data) and acyclic (e.g., events) data.
    • DataSetMetaData to define field names, data types, semantics, and metadata (e.g., units, ranges).
  2. Message Handling
    • DataSetMessages: Encode DataSets into payloads (key frames, delta frames or event frames)
    • Keep-Alive Messages: sent if no data is published within a configured interval.
  3. Entities
    • Publishers: Generate and send NetworkMessages via MQTT
    • Subscribers: Receive, filter and decode NetworkMessages; map DataSet fields to target variables.
    • Security Key services (SKS): Manages key distribution and authentication.
    • Configuration Tool: Configure Pub/Sub components via OPC UA or offline files.
  4. Transport Protocol Support
    • Broker-based Middleware: MQTT (v3.1.1 and v5) with support for topics, queues and QoS.
    • Transport Security: TLS for broker-based protocols;
  5. QoS and Timing
    • PublishingInterval: Configurable interval for cyclic data.
    • Priority Labeling: Mapping to network QoS mechanism.
    • Timing Offsets: sampling, publishing, receive and processing offsets for synchronization.
  6. Discovery Mechanisms
    • Multicast Discovery: UDP-based discovery probes/announcements for publishers.
    • Metadata Distribution: sending DataSetMetaData via NetworkMessages or OPC UA configuration methods.
  7. Error handling and Diagnostics
    • MessageReceiveTimeout: Detect missing messages and trigger error states.
    • OverrideValueHandling: Configure fallback values (e.g. last usable vale, static override)
    • Diagnostics Counters: Track sent/received messages, errors and security events.
  8. JSON-Specific Features
    • JSON NetworkMessage and DataSetMessage structure compliant with RFC 8254

2019

Aleph releases the first TMC Compliant Aggregation Server

Aleph releases AGS, the first TMC Compliant OPC UA Aggregation Server. As an aggregation server, AGS connects to multiple machines' OPC UA Servers and exposes the complete information set as one single aggregated server.

Customers use AGS to simplify and standardize their secure OPC UA connectivity, for example using AGS as a single secure entry point to a production line consisting of multiple machines each with its own OPC UA Server.

AGS is available as a Docker micro-service designed to run at the Edge that can be configured to aggregate only parts of the connected servers.

AGS is now available as a component of the Aleph FlexServer.

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2018

The OPC Foundation releases the Tobacco Machine Communication (TMC) OPC UA Companion Specification, OPC30060 authored by Aleph Digital Industry

The OPC Foundation releases for public use OPC 30060 OPC UA Companion Specification for Tobacco Machine Communication (TMC) Joint Working Group.

OPC 30060 is an OPC UA information model Aleph designed for the vertical adoption of OPC UA in the tobacco industry "for the common benefit of both OEM and cigarette manufacturers" as intended by the TMC Joint Working Group, whose members are British American Tobacco, Imperial Tobacco, Japan Tobacco International and Philip Morris International.


OPC 30060 provides the infrastructure for interoperability of both legacy and new tobacco machines, modeling functionality into digital machines with the following key components:

1. Material Flow
  • Material Loading Points: machine components where materials are identified, validated against the production specification, loaded into the machines and the relevant dispensing/consumption is accounted for.
  • Material Output Points: machine components where good product is released, identified, and the relevant inventory created.
  • Material Rejection Points: machine components where out-of-specification material is released and accounted for. In conjunction with Defect Detection Sensors, they identify quality issues.
  • Material Buffers: machine components where some material is temporarily stored in the machine, typically in the form of a small reservoir necessary to keep the flow uninterrupted.
  • Machine Module Production: a machine's digital component managing information about the production order running at the machine, including starting and stopping production.
2. Quality
  • Defect Detection Sensors: machine components or subsystems, including sensors and defect reasons, measuring quality parameters that may trigger the rejection of the product.
  • Process Items: process measurements with their high-speed statistics generated at the machine.
  • Machine Module Setup: a machine's digital component managing all the setup parameters for the product running at the machine.

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