Type
AI-Powered Aviation Safety System
Objective
A large aviation company came to Adorebits with an intent to design an all-inclusive AI-powered aviation safety system. They sought to harness the most advanced analytics, machine learning, and IoT technologies to contribute towards both improving safety and optimizing their operations, in addition to generating predictive insights.
Technologies
Python, SAS, R, Tableau, AWS, IoT, Docker, Jenkins
Country
Germany
Project Attributes
Engagement Model
Dedicated Team Engagement
Duration
18 months
App Users
Aviation safety officers, maintenance staff, flight operation managers, and ground crew
Challenges
- The complexity of the safety data: This client was handling massive safety
data coming from many sources such as aircraft sensors and the maintenance
logs coupled with flight operations. Consolidating fractured data sources into a
coherent system was an issue. - Predicting Safety Risks: It does not have the predictive power or early
detection of safety risk. - Real-Time Data Processing: It lacked the infrastructure to be able to process
large amounts of real-time data from IoT devices. - Regulation Compliance: It was a pretty regulatory industry. The system needed
to comply with global standards of safety while simultaneously increasing the
overall processes of safety. - Scaling issues: The airline was expanding its fleet as well as operations; thus,
the existing system could not scalably accommodate the number of data.
Solutions
- Data Integration using Python and SAS: Adorebits used Python when
developing these machine learning models meant for the analysis of safety data,
but advanced data management and analytics were done in SAS. The integration
of both technologies allowed for processing enormous amounts of historical and
real-time data to identify and predict patterns. - Predictive Analytics with R and Tableau: Adorebits used predictive analytics
with R to develop models that would make safety-risk predictions. This was
based on historical flight data, maintenance records, and real-time operational
information. The dashboards explained the visual analysis that enabled better
monitoring of safety trends and proactive actions by the safety officers. - Deployed on AWS: The service helped realize scalability and security
requirements for large amounts of safety-related aviation data. This cloud
infrastructure ensured scalability. - Real-time monitoring with IoT: IoT sensors installed on aircraft continuously
stream real-time data such as engine health, fuel levels, and system
performance in real time. Such information was analyzed in real-time on AWS. - Continuous Integration using Docker & Jenkins: The use of Docker helped
make the application easier to containerize for multiple environments, and it was
automating the CI/CD pipeline using Jenkins, providing continuous updates, bug
fixes, and improvements in the system.
Results:
- Improved Safety Monitoring: An AI-based system improved the real-time monitoring of the safety parameters to the extent of 50% alerting on time the safety officer before the risks turned into emergencies.
- Reduced Maintenance Downtime: Predictive analytics driven by Python and SAS, reduced unscheduled maintenance by 25%.
- Regulatory Compliance: The system ensured that it was absolutely in full compliance with the international aviation safety regulations; the safety audit process became streamlined.
- Enhanced Decision: Timely data processing and predictive analytics allowed the flight operation managers to make much smarter decisions
- Cost Optimization: Due to Hadoop and AWS infrastructure it used for optimizing data processing, the operational cost of safety management saved 15%.
Conclusion:
Adorebits’ AI-Powered Aviation Safety System took the approach that the client had been considering for safety in aviation to a completely new level, making it a leader in innovation in the sphere of aviation safety.