How L.B. Bohle detects deviations on pharmaceutical process equipment early
Process, energy and maintenance data come straight from the PLC. Machine learning models detect deviations from the ideal state early – on premises too, as pharmaceutical production requires.
- Customer
- L.B. Bohle Maschinen und Verfahren GmbH
- Industry
- Pharmaceutical process equipment
- Services
- IIoT solution, data acquisition, machine learning
- Technology
- Industrial Data Platform, anomaly detection, on-premises capable
Starting point
L.B. Bohle builds process equipment for the pharmaceutical industry. The goal was a future-proof industrial IoT system that gives L.B. Bohle and its customers full transparency over the machines – as the basis for continuously improving maintenance and energy efficiency.
In pharmaceutical production in particular, data is sensitive. The solution therefore has to be able to run on premises, without a cloud, at the customer’s site.
Implementation
An IIoT solution that can run on premises was built on codestryke’s Industrial Data Platform. It captures process, energy and maintenance data straight from the PLC – starting with a pilot machine in L.B. Bohle’s service center.
The data is structured: time series, events, batches and parameter changes, each with recipe and user.
Machine learning models detect deviations from the ideal state early and provide trend forecasts for process, maintenance and energy – for example for compaction force, gap width and drives.
- Data acquisition straight from the PLC
- Time series, events, batches and parameter changes with recipe and user
- Anomaly detection and trend forecasts for process, maintenance and energy
- On-premises capable for sensitive pharmaceutical production data
Result
Maintenance and energy data are always in view: drive temperatures, torques, operating hours and consumption per batch. Deviations stand out early, and the knowledge about the machine is secured in the data.
- Performance
- +12 %
- Knowledge secured
- 100 %
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