Predictive Maintenance
Predict failures instead of maintaining by schedule or after a fault — based on vibration, temperature and operating data.
What Industrial AI is
Industrial AI refers to the use of AI methods in core industrial processes: manufacturing, maintenance, quality assurance, intralogistics and production planning. Unlike generative assistants in the office, Industrial AI works mainly with time series, images and event data from machines and plants.
The difference from classic automation lies in handling uncertainty: rule-based controls do exactly what was programmed. AI models recognise patterns in data that are too complex for fixed rules — such as the gradual wear of a bearing or the combination of process parameters that causes scrap.
Typical fields of application
The most viable use cases in mid-sized companies are rarely spectacular; they solve concrete bottlenecks in everyday production.
Visual quality inspection
Camera-based detection of surface defects, assembly errors or foreign objects directly in the line.
Process optimisation
Recognise relationships between parameters and result quality and adjust setpoints based on data.
Production planning
Optimise sequences, setup times and material requirements under real constraints.
Energy management
Forecast load peaks and shift energy-intensive steps into favourable time windows.
Documentation & knowledge
Open up maintenance histories, fault reports and manuals for shift staff through AI assistants.
Data and infrastructure as the foundation
Industrial AI stands or falls with data access. In many plants the relevant data already exists — in PLC, MES, ERP or shop-floor data systems — but is not consolidated, not time-synchronised or not historised.
In the IT check-up we examine which data sources exist, how they can be connected and which gaps must be closed before a model. Often the first step is not AI but clean data preparation.
- Connecting machines via OPC UA, MQTT or existing interfaces
- Time-series storage and historisation as the basis for models
- Edge processing close to the machine where latency or data protection require it
- Operation on premise, in the cloud or hybrid — depending on TCO, security and requirements
Security, data protection and standards
Production data is a trade secret. We design Industrial AI systems so that data only leaves the company when explicitly intended — and so that requirements from ISO 27001, TISAX or BSI baseline protection are considered from the start.
For AI as a safety component of machinery, the high-risk requirements of the EU AI Act will apply (Annex I, from August 2028). We classify every use case early so documentation and human oversight are planned in time.
Economics per use case
Every Industrial AI use case is calculated with the Sellium TCO model: data connection, model operation, hardware, energy and maintenance are set against the measurable benefit from avoided downtime, less scrap or lower energy use. The result is a prioritisation your controlling can follow — before investing.