Intelligent Maintenance, Maximum Vehicle Availability
powered by AI
KEBA Digital uses Artificial Intelligence to detect maintenance needs in truck fleets early. This keeps vehicles on the road longer, reduces costs, and makes planning more reliable. By predicting issues before they occur, we turn downtime into uptime and keep your logistics moving efficiently.
The Challenge:
Unexpected Breakdowns and Costly Downtime
In transport logistics, unplanned vehicle failures are a major cost driver: downtime causes delivery delays, customer dissatisfaction, and high replacement costs. At the same time, rigid maintenance intervals lead to unnecessary and inefficient resource usage.
What’s missing is a predictive maintenance strategy that analyzes and forecasts actual vehicle conditions – instead of following static schedules based on mileage or time.
The Solution:
Predictive Maintenance with AI
KEBA Digital develops AI models that analyze sensor, telematics, and operational data in real time. This enables early detection of potential defects or wear – before they cause breakdowns.
Our solution empowers data-driven maintenance decisions, optimizes workshop capacity, and extends vehicle lifespan.
Real-Time Condition Monitoring – Stay Safe on the Road
AI continuously monitors engine, tire, brake, and temperature data. Patterns indicating potential issues are automatically detected.
Benefits:
- Early detection of wear or malfunctions
- Reduced downtime through planned maintenance
- Lower maintenance costs through targeted interventions
Maintenance Demand Prediction – Data-Driven and Precise
AI calculates probabilities for component failures based on usage intensity, load, environmental factors, and historical error data. This allows forecasting the ideal maintenance time to minimize costs and downtime.
Beneftis:
- Accurate planning of service intervals
- Optimal utilization of workshops and technicians
- Extended fleet lifespan
Model Comparison for Failure Prediction: ROC-AUC Curve
The ROC-AUC curve shows how reliably different models can predict truck failures. It is a key tool for evaluating the quality of predictive maintenance algorithms and finding the optimal balance between early maintenance and unnecessary interventions..
Why it matters:
Model performance comparison: Identifies the most accurate prediction method
Optimal thresholds: Helps balance sensitivity and specificity
Understanding Sensor Data: The Foundation for Predictive Maintenance
The scatterplot matrix illustrates relationships between numerous air pressure system measurements in truck fleets. Such analyses reveal patterns and correlations critical for failure prediction. Visualizing complex data uncovers connections that indicate potential critical states – creating a solid basis for data-driven maintenance strategies.
Why it matters:
Detect critical correlations: Shows which parameters strongly interact
Highlight anomalies: Outliers may indicate upcoming failures
Feature selection for AI models: Basis for precise failure predictions
More transparency: Complex sensor data becomes understandable and actionable
Technological Foundation
KEBA Digital combines data integration, AI models, and edge computing into a scalable solution for fleets of any size.
- Machine Learning (Random Forest, XGBoost)
- Deep Learning (von LSTM bis hin zu KAN und NBEATS)
- Anomaly Detection
- Edge Computing
- Predictive Analytics
Your Benefits
with KEBA Digital
Cost Reduction
Maintenance based on actual needs instead of fixed intervals
Efficiency Boost
Early defect detection prevents downtime
Sustainability
Less wear, lower energy consumption
Productivity Boost
Higher vehicle availability and predictable operations
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