Loading data

Siemens Cham, Germany - Reduced unplanned downtime with Senseye Predictive Maintenance

Siemens Cham, Germany - Reduced unplanned downtime with Senseye Predictive Maintenance

Electrical & Electronic (E&E)

Siemens AG

Reduce unplanned downtime in a highly automated production environment at the Siemens factory in Cham (Bavaria).
Improve productivity, asset transparency, and maintenance efficiency across critical motor‑driven components.
Transition from reactive or time‑based maintenance to predictive maintenance using real‑time insights.
Achieve a solution that works in a brownfield setup, avoiding major overhauls and integrating into existing systems.
Increase equipment reliability, maintaining consistent output and preventing negative downstream effects such as customer dissatisfaction and productivity losses.
Build a scalable approach that can be rolled out to other production lines and locations.

Implementation of a combined hardware and software digitalization approach that bridges the shop floor and IT levels.
Smart Siemens hardware installed on existing equipment:

SIRIUS 3RC7 ILM (Intelligent Link Modules)
SIRIUS 3RW5 Soft starters
SIMATIC ET 200SP e-Starter
Full portfolio of motor monitoring and control devices capturing data such as torque, current, voltage, temperature, energy, cycles, runtime, etc.

Hardware connected seamlessly within the TIA Portal / Step 7 environment for fast integration.
Pre‑processing logic added to the PLC for secure and structured data transfer.
Deployment of Senseye Predictive Maintenance:

AI‑based cloud solution analyzing time‑series data.
Detects anomalies, thresholds, patterns and produces explanations, root‑cause suggestions, and remedy recommendations.
Provides maintenance technicians with remote access, case views, evidence, and a Co‑Pilot chat for guided troubleshooting.

Siemens Services supported the setup using standardized blueprints, KPI definition workshops, asset onboarding support, and implementation guidance.

Significant reduction of unplanned downtime by predicting failures before they occur.
Clear visibility into the health and condition of more than 90 assets in the pilot line.
Early detection prevented several production stoppages—each potentially costing several days of lost output.
Improved maintenance efficiency:

Increased production reliability, customer satisfaction, and internal productivity.
Cost savings through proactive maintenance (avoiding 3–5× higher costs linked to unplanned breakdowns).
Solution implemented with minimal disruption to existing operations (brownfield retrofit).
Scalable approach now being rolled out to other lines in Cham, Amberg and additional Siemens factories.