Knowledge

The Knowledge Center explains the technical and specialized concepts behind Bytefabrik. The content traces the path from heterogeneous machine data through a robust IIoT platform to the systematic analysis and improvement of production.

Data Modeling

A reliable analysis begins with a shared understanding of the data. Raw signals are linked to the plant and process context and converted into stable technical logs.

• Data modeling explains the fundamental principles of modeling and the process of transforming technical signals into data that can be used for business purposes.
• "Raw Data & Metadata" covers data structures, modeling approaches, and the description of technical signals.
• The production log links parts, activities, process parameters, quality, and timing.
• The machine status report details production times, downtime, and other system statuses.
• The event log standardizes malfunctions, alarms, and warnings.
• KPI models define transparent metrics based on these logs.

IIoT Platforms

An IIoT platform provides the technical foundation for connecting machines and systems in a reusable way, structuring data, and making it available for various applications.

IIoT Platforms: Architecture, Features, and Selection Criteria Explained Reference architecture, connectivity, edge and cloud operations, and the key criteria for selection and implementation.

Apache StreamPipes

Apache StreamPipes is the open-source technological core of the Bytefabrik IoT Data Hub. This knowledge base deliberately separates the specific open-source technology from the general selection and architectural considerations of an IIoT platform.

"Apache StreamPipes: Architecture, Features, and Use Cases" explains how data sources, pipelines, storage, and visualization work together, and what role extensibility and productive operation play.

Production Analysis & Improvement

Using the shared database, it is possible to identify losses, pinpoint their causes, and assess the effectiveness of measures.

• Production Efficiency & OEE explains availability, performance, and quality, as well as the process from identifying deviations to demonstrating effectiveness.
• Identifying production problems provides a pragmatic path from the visible symptom to the technically verified cause.
• Production analysis using machine data links key performance indicators to machine status, alerts, process parameters, and product context.
• Process analysis using machine data reconstructs actual workflows and highlights variations, loops, transitions, and wait times.