45% less unplanned downtime with predictive maintenance
A major manufacturer reduced machine failures by 45% by combining sensor data with machine learning for predictive maintenance.
0%
Less downtime
€0.0M
Annual savings
0%
Detection accuracy
0
Machines monitored
- Client
- Manufacturing Company
- Sector
- Manufacturing
- Result
- 45%Less downtime
Background
The challenge
Unplanned machine downtime cost the company millions per year. Reactive maintenance led to expensive repairs and production delays.
Key problems
- Average of 12 unplanned downtimes per month
- High costs for rush parts and overtime
- Production schedules were constantly disrupted
- No insight into machine health until it was too late
Methodologie
Our approach
We implemented an end-to-end IoT analytics platform that analyzes sensor data in real-time and proactively alerts maintenance teams.
01
Sensor Integration
Connection of 340 machines via IoT gateways with central data lake for vibration, temperature, and power consumption data.
02
Anomaly Detection
Training of unsupervised learning models for automatic detection of abnormal patterns.
03
Failure Prediction
Development of remaining useful life (RUL) models for specific failure modes.
04
Alerting & Dashboards
Real-time monitoring dashboards and automated alerts to maintenance teams.
Impact
Results
The predictive maintenance system was rolled out within 4 months and delivered immediate results.
- 0%
Less unplanned downtime
From 12 to 7 downtimes per month
- €0.0M
Annual savings
Through lower maintenance costs and higher uptime
- 0 weeks
Average warning time
Time to plan maintenance before failure occurs
- 0%
True positive rate
High accuracy minimizes false alarms
“We can now plan maintenance instead of reacting to problems. This has not only saved costs but also reduced the workload on our team.”
Plant Manager
Manufacturing Company
Technologies
Tools & platforms used
- Python
- Apache Kafka
- InfluxDB
- TensorFlow
- Grafana
- Azure IoT Hub
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