Safety (and maintenance) improVEment Through automated fLight data ANAlysis

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).

Innovation aspects

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.

Strategy targets

An efficient and integrated mobility system improving service quality and reliability.