BAGXPERT Predictive Maintenance at Christchurch Airport
Revolutionising BHS Maintenance Activities with our BAGXPERT Predictive Maintenance Solution
About this Project
- Location: Christchurch, New Zealand
Background
Airports around the world face the constant challenge of maintaining efficient and reliable baggage handling systems. The complexity of these systems, combined with the critical need for uninterrupted operation, makes it essential to adopt innovative solutions for monitoring and maintenance. Alstef, a leader in airport automation solutions for over 60 years, has introduced BAGXPERT, a predictive maintenance solution that can be retrofit to existing conveyor systems.
This case study explores how BAGXPERT has transformed baggage handling operations at Christchurch International Airport, leading to increased efficiency, reduced downtime, and significant cost savings.
Key Features of BAGXPERT:
IOT Motes: Installed at each asset, these motes relay data via a radio signal to a gateway and then into the cloud via a SIM card. Each gateway can receive signals from up to 100 IOT motes.
Comprehensive Sensors: The motes have sensors which are attached to various components of critical assets to monitor parameters such as:
- Motor temperature
- Gearbox temperature
- Indoor and outdoor temperature
- Conveyor speed
- Bag throughput
- Energy usage
- Belt alignment
- Vibration
AI-Driven Predictive Maintenance: The collected data is analysed using AI software, which not only identifies potential failures but also predicts them well in advance, allowing for proactive maintenance planning.
Energy Efficiency Optimization: By analysing energy usage data, BAGXPERT enables adjustments to system speed, timing, and other parameters to optimize energy efficiency of the BHS system. Upgrading to more energy-efficient motors, belts, and synthetic oils further reduces energy consumption and operational costs.
Implementation
The implementation of BAGXPERT to the existing Christchurch International Airport BHS system began in 2023 and involved the following steps:
Retrofitting: IOT motes were installed on existing conveyor systems without any disruption to ongoing operations.
Data Integration: Data from the motes was seamlessly integrated into the cloud via gateways, enabling real-time monitoring and analysis.
AI Training: Historical data from other BAGXPERT implementation sites and real-time data were used to train the AI software for accurate predictive maintenance insights.
Optimization: Continuous monitoring allows for ongoing adjustments to improve, prediction accuracy, energy efficiency and reduce operational stress on equipment.
Results
The deployment of BAGXPERT led to remarkable improvements in the airport’s baggage handling operations:
- Reduced Downtime: Predictive maintenance capabilities significantly decreased unplanned downtime and maintenance requirements ensuring smoother operations and improved passenger satisfaction.
- Cost Savings: Optimised energy usage and reduced spare parts inventory resulted in notable annual savings.
- Environmental Impact: Energy efficiency improvements and reduced carbon footprint align with the airport’s sustainability goals, enhancing its reputation as an environmentally responsible entity.
- Enhanced Operational Efficiency: Real-time data insights allowed for better decision-making and more efficient baggage handling processes.
Conclusion
BAGXPERT has proven to be a game-changer for the airport’s baggage handling system. By providing advanced monitoring and predictive maintenance capabilities, it has not only reduced operational costs and downtime but also contributed to the airport’s sustainability efforts. Alstef, through BAGXPERT, continues to be a valuable partner in enhancing the efficiency and reliability of airport operations, positioning itself as a key player in the future of airport automation and sustainability.
