Early failure detection
Real-time sensor insights
Scalable, flexible model
Adaptive ML efficiency
The challenge
Predictive maintenance with sensor analytics
A European engineering company collected continuous sensor data from multiple turbines. To enhance efficiency, it aimed to develop an algorithmic workflow for automatic gearbox anomaly detection using sensor data.
Key challenges
Integrating data from multiple sensors
Predicting machine failures at an early stage
Deploying a real-time solution for high-velocity data
The solution
Optimized gearbox monitoring
Data and classification
Analyzed sensor data
Categorized trends patterns
Assessed model performance
Anomaly detection
Dual-model analysis
Filtered false alarms
Score-based detection
Implementation approach
1
Model deployment
Seamless rollout
High-speed processing
Real-time integration
2
Alerts and thresholds
Triggered alerts
Set detection limits
Enabled proactive maintenance
3
Continuous improvement
Refined models
Adjusted thresholds
Help improve accuracy
The impact
Streamlined operations and cost savings
Cost savings
Early failure detection
Maintenance savings
Scalable deployment
Real-time insights
Real-time predictions
Adaptive learning
Faster model building
Adaptive model efficiency
Continuous model improvement
Improved efficiency
Streamlined production
Looking ahead
Broader equipment coverage
Expand anomaly detection to more equipment types
Predictive maintenance growth
Integrate predictive maintenance across additional production lines
Ongoing model refinement
Continuously refine machine learning models for improved precision
