Involvement of important EU and Russian Federation industries and research organisations from the aviation area, into a common ambitious effort to enhance flight operational safety and initiate smart maintenance through intelligent and automated Flight Data Monitoring (FDM).
The SVETLANA project aimed to provide an advanced smart update of flight safety by:
- Deploying an automated and standardised flight data management cycle capable of routinely processing large amounts of data to allow operators to thorougly examine all flight data using advanced sophisticated algorithms;
- Proposing a common standard methodology for flight data analysis using singular points automation based on data from various systems and operators to be combined and processed by advanced algorithms. Analysis on a larger statistical footprint with data from various aircraft types or operators would become comparable and could be combined;
- Identifying, detecting and correcting potentially unsafe trends before they can manifest themselves in an incident, using self-learning adapted methodologies;
- Performing a study of potential parameters or assessment techniques that can help in predicting the condition or failure of hardware on the basis of current FDR data, and can reduce the amount of corrective maintenance and provide better insight for predictive maintenance cycles;
- Limiting the need for specialist involvement in the FDM cycle to the decision making process only;
- Providing insight into abnormal events in order to adjust training, maintenance and procedures to prevent recurrence;
- Establishing isolation criteria templates for well-known conditions and situations from large simulated datasets, allowing a broader and earlier identification of abnormal conditions;
- Validating and assessing the advanced FDM Methodology in a simulated/synthetic environment;
- Providing a common standardised solution that has acceptance in both the EU and the Russian Federation.
Involve European and Russian industries and research organisations to create a common ambitious effort to enhance operational safety and initiate smart maintenance.
Among the final noticable results:
- Flight data pre-processing: which includes flight phases identification and detection improvements, new parameter validity technique, handling of data flaws (gaps, blanks, …), sub-system definitions;
- As mentioned above, the whole flight data workflow, with common data formats;
- Aircraft independent and automated event detection rules;
- Data aggregation from various sources, including ACMS;
- New generation HMIs to combine the analysis from various sources;
- Reusable components of the prototype architecture: data converters, data base structure, data loaders;
- Novel methods, algorithms, software for real-time analysis of data streams, successfully reused in other applications (medical environment).
1. Requirements and FDM definitions / standards - this phase of the project resulted in:
- Improvement of the current "historical" event-based strategy for FDM;
- Development of new strategies for FDM such as data mining methods;
- Adding of external and contextual information to FDM data;
- Improvement of automation for FDM data processing.
2. Flight Data Management Cycle Process which led to:
- The definition of a common data format for flight data, available for Russian and European legacy systems;
- The definition of a common data format for analysis results, as a preparation to a dual exploitation with currently used analysis tools.
3. Knowledge extraction from data streams - among the main objectives that were achieved are:
- A number of data-mining technologies were investigated, developed and tested;
- Identification and testing of an algorithm for anomaly identification (online and offline);
- Development of an algorithm demonstrator with reinforcement\online learning capability);
- Development of additional software tools for inter-module communications.
4. Enhanced self-inspection - the following algorithms were developed:
- Filtering by a hard filter using wavelet transform at level 5 (best value in the experiments) and find maximum absolute derivatives of the resulting data. This is used to detect high-amplitude signals;
- Marking as a wrong source the data point where the derivative is greater than the max derivative.
An efficient and integrated mobility system improving service quality and reliability.