Airports rely on the smooth, uninterrupted operation of their baggage handling systems (BHS) to maintain passenger flow and avoid costly disruptions.
Predictive maintenance, especially through Remaining Useful Life (RUL) predictions, offers a powerful approach to reducing unnecessary maintenance and parts consumption. However, to fully realise its potential, airports and OEMs must go beyond traditional single-source models.
The fusion of high-frequency sensor data with event-driven machine logs creates a more robust, accurate method for predicting failures and optimising system reliability.
The Power of Feature Engineering Across Heterogeneous Data Sources
BHS systems generate sensor data (e.g., temperature, vibration, motor currents) at high frequency, offering continuous insights into system performance. Alongside this, machines produce event-driven logs—structured records of system states, errors, and operational changes. While sensor data reflects physical measurements in real time, these logs capture discrete system events with rich operational context but irregular sampling.
Analysed in isolation, each data source has limitations: sensor data may overlook complex event patterns, while event logs lack the granularity to detect subtle degradation. Combining them through multi-modal data fusion transforms raw data into actionable insights. This involves advanced feature engineering, where raw sensor streams and event records are converted into unified metrics, trends, and patterns. Aligning anomalies in sensor readings with preceding or concurrent events—for example, an increased motor temperature following a “belt misalignment” log entry—creates a comprehensive view of system behaviour.
Handling Temporal Misalignment
Fusing sensor data with event-driven logs introduces temporal alignment challenges. Sensor data may be sampled every second, while event logs are created based on discrete machine events, often at irregular intervals. Addressing this requires techniques such as:
- Time windowing, aggregating sensor data over windows that align with event timestamps.
- Event interpolation, assigning event states to corresponding sensor data windows, ensuring context is preserved.
- Timestamp normalisation, synchronising both streams on a unified timeline to capture correlations effectively.
These methods enable predictive models to interpret sensor anomalies in the context of preceding or concurrent machine events, significantly enhancing RUL predictions.
Dimensionality Reduction for Complex Systems
Baggage handling systems involve thousands of sensors and diverse event log streams, resulting in high-dimensional data. Without effective dimensionality reduction, predictive models risk overfitting and inefficiency. Techniques such as Principal Component Analysis (PCA), autoencoders, or t-SNE distil these complex data sets into compact, informative representations.
Notably, fusing sensor data with event logs often reveals latent patterns—such as a recurring sequence of minor alerts that precede noticeable sensor degradation. Incorporating these patterns enhances model robustness, interpretability, and early warning capabilities.
Case Study: Event Patterns Preceding Sensor Degradation for better baggage handling systems predictive maintenance
At a leading airport we tested this multi-modal data fusion in its BHS predictive maintenance strategy. Analysis revealed that repeated event logs in specific system codes often preceded measurable increases in other sensor data —an early indicator of impending failures. By combining these discrete event logs with continuous sensor monitoring, the predictive system identified degradation trends days in advance.
The result was a 33% decrease in parts consumption, demonstrating how combining event-driven logs with sensor data not only improves system reliability but also supports sustainability and cost-efficiency goals.
Practical Implementation: API Architecture for Real-Time Data Fusion
Deploying multi-modal data fusion in a live airport environment demands a flexible, robust API architecture capable of integrating diverse data streams. Key components include:
- Data ingestion APIs to collect and normalise both sensor and event log data from machine sources.
- Real-time processing frameworks (e.g., Apache Kafka, Spark Streaming) to synchronise data streams and execute fusion logic.
- Edge computing for localised pre-processing, enabling rapid anomaly detection and response.
- Cloud-based analytics platforms support scalable machine learning models for RUL prediction and operational insights.
This architecture ensures that predictive maintenance systems can process data in real-time, empowering service engineers to make proactive, data-driven decisions.
The Future of BHS Reliability: Smarter Maintenance with Data Fusion
Integrating event-driven machine logs with continuous sensor data unlocks new levels of reliability in BHS operations. Multi-modal data fusion enhances early failure detection, reduces unnecessary parts replacement, and maximises system uptime—all vital outcomes in modern airport operations.
For BHS OEMs, embedding these advanced predictive capabilities into new systems meets the growing demand from airports for performance-based contracts and reliability-focused solutions.
Ready to Embrace the Future of Predictive Maintenance at Airports?
At Amygda, we’re experts in predictive maintenance solutions for airports, that fuse high-frequency sensor data with event-driven machine logs to optimise baggage handling system reliability. If you’re ready to leverage data fusion for smarter maintenance, reduced costs, and improved uptime, we’d love to show you how.
If you are an OEM trying to meet an RFP requirement for predictive maintenance at airports or predictive maintenance for baggage handling systems, let’s talk!
Get in touch today (email me at [email protected]) to discover how we help with predictive maintenance at airports.
