Self-Healing Data Streams Using Multiple Models of Analytical Redundancy

We have created a highly declarative programming language called PILOTS that enables error detection and estimation of correct data streams based on analytical redundancy (i.e., algebraic relationship between data streams). Data scientists are able to express their analytical redundancy models with the domain specific grammar of PILOTS and test their models with erroneous data streams. PILOTS has the ability to express a single analytical redundancy, and it has been successfully applied to data from aircraft accidents such as Air France flight 447 and Tuninter flight 1153 where only one simultaneous sensor type failure was observed. In this work, we extend PILOTS to support multiple models of analytical redundancy and improve situational awareness for multiple simultaneous sensor type failures. Motivated by the two recent accidents involving the Boeing 737 Max 8, which was potentially caused by a faulty angle of attack sensor, we focus on recovering angle of attack data streams under multiple sensor type failure scenarios. The simulation results show that multiple models of analytical redundancy enable us to detect failure modes that are not detectable with a single model.

[1]  G. Hardier,et al.  Aerodynamic model inversion for virtual sensing of longitudinal flight parameters , 2013, 2013 Conference on Control and Fault-Tolerant Systems (SysTol).

[2]  Karen Willcox,et al.  Multifidelity DDDAS Methods with Application to a Self-aware Aerospace Vehicle , 2014, ICCS.

[3]  Busyairah Syd Ali,et al.  Automatic Dependent Surveillance Broadcast (ADS-B) , 2017 .

[4]  A. Strauß.  Theory Of Wing Sections Including A Summary Of Airfoil Data , 2016 .

[5]  Inseok Hwang,et al.  A Survey of Fault Detection, Isolation, and Reconfiguration Methods , 2010, IEEE Transactions on Control Systems Technology.

[6]  Carlos A. Varela,et al.  Dynamic data-driven learning for self-healing avionics , 2017, Cluster Computing.

[7]  Marcel Staroswiecki,et al.  Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Erik Blasch,et al.  Airplane flight safety using error-tolerant data stream processing , 2017, IEEE Aerospace and Electronic Systems Magazine.

[9]  J. Tinsley Oden,et al.  Virtual model validation of complex multiscale systems: Applications to nonlinear elastostatics , 2013 .

[10]  James Llinas,et al.  High Level Information Fusion (HLIF): Survey of models, issues, and grand challenges , 2012, IEEE Aerospace and Electronic Systems Magazine.

[11]  Frederica Darema,et al.  Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements , 2004, International Conference on Computational Science.

[12]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[13]  Alessandro Galli,et al.  Dynamic Data-driven Avionics Systems: Inferring Failure Modes from Data Streams , 2015, ICCS.

[14]  Hans-Dieter Joos,et al.  Enhanced detection and isolation of angle of attack sensor faults , 2016 .

[15]  Carlos A. Varela,et al.  Self-Healing Spatio-temporal Data Streams Using Error Signatures , 2013, 2013 IEEE 16th International Conference on Computational Science and Engineering.