Ignition-Powered UNS Establishes Single Source Of Truth For Aerospace Manufacturer

Project Overview

BMT Aerospace has to keep up with innovative software solutions in order to provide quality services and grow as a company. However, with a variety of interconnected softwares across four sites, BMT Aerospace struggled to maintain consistency. With the help of AT-Automation, BMT Aerospace implemented a robust Unified Namespace (UNS) to establish a single source of truth across the organization.

Challenges

BMT Aerospace operated a complex landscape of systems and integrations that were difficult to maintain. This led to a lack of clarity regarding system interdependencies — nobody truly knew what was connected to what, which versions were running where, where critical data was stored, or how well it was protected.

Additionally, the environment contained "ghost code" from unmaintained, obsolete software that was still in operation. This created a significant risk because upgrading the system could potentially cause production downtime, which discouraged system updates altogether, thereby increasing security vulnerabilities.

As such, security was another major concern, with uncontrolled inbound and outbound connections and reliance on opaque, proprietary cloud services. Furthermore, making changes to the system — whether adding, removing, or replacing components — required BMT Aerospace to perform simultaneous development across multiple systems, making the process error-prone and inefficient.

Complicating matters further was the simultaneous replacement of BMT Aerospace’s ERP system during the Ignition integration, adding another layer of complexity to data management and process synchronization and requiring careful planning to determine which system held master data and which needed production feedback.

Ignition was also tasked with acting as the shop floor controller. In this role, the application had to handle large volumes of data, much of which would become part of the UNS. However, it soon became apparent that the initial UNS design was not future-proof. Joint training sessions between AT-Automation and BMT Aerospace led to new insights and a complete redesign of the UNS.

A few months later, BMT Aerospace initiated a major reconfiguration of their factory layout, which triggered another UNS rebuild. BMT Aerospace also requested the implementation of a competence management system within Ignition. This functionality was developed in parallel with the UNS, extending the platform’s value beyond process control.

Solution

Ignition is now a central component of the entire system, playing a pivotal role in both data acquisition and visualization with a full suite of interface options.

Ignition collects data at the edge and transmits it upstream using integrated MQTT functionality. The edge functionality is deployed on a Beckhoff IPC, which integrates PLC capabilities with a Windows environment running Ignition Edge. These IPCs are added in situations where the machine does not natively support or allow access to the required data. 

Upon startup, the PLC reads all available I/O and requests sensor configuration details from Ignition. The Ignition frontend server manages these configurations, stores them in a database, and transmits them to the edge device via MQTT from the backend server. The data collected by the IPCs — primarily vibration and power measurements — is sent in JSON format and processed on the backend server for storage. A central Ignition server aggregates this data from the MQTT broker and builds a complete UNS, which is then published to Canary for historical storage. This allows users to access a real-time or historical snapshot of the factory state at any point, empowering BMT Aerospace with valuable analytical capabilities.

The project’s uniqueness lies in its architecture and scope. It’s a complete, real-time data ecosystem where any stakeholder can subscribe to the UNS and access consistent, contextualized information.

Result

AT-Automation has developed a shop floor controller application that centralizes data collection and storage for BMT Aerospace, significantly reducing paperwork across sites. The UNS provides a single source of truth that can be accessed by anyone who subscribes to it. MQTT ensures efficient, decoupled data sharing, and Canary Labs provides seamless historical access.

One notable success occurred during internal testing. An engineer, testing the end-to-end pipeline — from Ignition Edge through dashboards and historical data in Canary — discovered an inefficiency in a drilling process. The data showed that the first of four drilling steps encountered no resistance, meaning the drill was effectively cutting air. By adjusting the process, production time for that task was reduced by 25%.

Further optimizations and benefits are expected to emerge as more systems and users adopt the new UNS structure. BMT Aerospace is aware that the road to innovation and Artificial Intelligence has six steps:

• In order to apply Artificial Intelligence in BMT Aerospace’s factories, they require large quantities of data. That is why the first two steps in the road to Industry 4.0 are computerization and connectivity. With computerization, BMT Aerospace can transform all of their documents and transactions from paper to digital. To accomplish this, they will use an ERP system and a document management system.
• In the next two steps, BMT Aerospace will start building a digital twin of their factory. Using the data from the first two steps will make it possible to keep a model of the factory up to date. This will allow BMT Aerospace to know exactly what is happening on the factory floor. The next step is to create transparency by leveraging analytics to interpret and understand the data being collected. Here, BMT Aerospace will already see the first artificial intelligence in the form of clustering, pattern recognition, and anomaly detection.
• In the final two steps, BMT Aerospace will continue improving their digital twin, which will evolve from a digital shadow to a closed-loop system. A digital shadow increases visibility and provides a possible best action. A closed-loop system also takes the action. Using neural networks, BMT Aerospace will train algorithms to predict the best next action. And using reinforcement learning, the algorithm will take action and learn from its results. The shop floor can then become fully autonomous and self-optimizing.

The value or ROI will increase exponentially as BMT Aerospace progresses in this journey toward Industry 4.0. The first few steps are very costly (ERP, MES and IIoT) and may not bring an immediate equivalent profit, but by laying this foundation BMT Aerospace will be able to evolve into a smart factory leveraging the power of artificial intelligence.

Start Date: December 2023

Deploy Date: January 2025