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Dynamic data-driven learning for self-healing avionics

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Abstract

In sensor-based systems, spatio-temporal data streams are often related in non-trivial ways. For example in avionics, while the airspeed that an aircraft attains in cruise phase depends on the weight it carries, it also depends on many other factors such as engine inputs, angle of attack, and air density. It is therefore a challenge to develop failure models that can help recognize errors in the data, such as an incorrect fuel quantity or an incorrect airspeed. In this paper, we present a highly-declarative programming framework that facilitates the development of self-healing avionics applications, which can detect and recover from data errors. Our programming framework enables specifying expert-created failure models using error signatures, as well as learning failure models from data. To account for unanticipated failure modes, we propose a new dynamic Bayes classifier, that detects outliers and upgrades them to new modes when statistically significant. We evaluate error signatures and our dynamic Bayes classifier for accuracy, response time, and adaptability of error detection. While error signatures can be more accurate and responsive than dynamic Bayesian learning, the latter method adapts better due to its data-driven nature.

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Notes

  1. We have designed, but not fully implemented the trainer abstraction in PILOTS version 0.4 as of June 2017.

  2. We have not implemented the when clause support in PILOTS version 0.4 as of June 2017.

  3. Note the current version (v0.4) of PILOTS does not fully support the grammar shown here as of October 2017.

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Acknowledgements

This research is partially supported by the DDDAS program of the Air Force Office of Scientific Research, Grant No. FA9550-15-1-0214, NSF Grant No. 1462342, and a Yamada Corporation Fellowship. We would like to thank anonymous reviewers for their valuable feedback. We would also like to acknowledge co-authors of previous PILOTS papers: Erik Blasch, Richard Klockowski, Alessandro Galli, Frederick Lee, and Colin Rice.

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Authors and Affiliations

  1. Department of Computer Science, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY, 12180, USA

    Shigeru Imai, Sida Chen, Wennan Zhu & Carlos A. Varela

Authors
  1. Shigeru Imai
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  2. Sida Chen
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  3. Wennan Zhu
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  4. Carlos A. Varela
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Corresponding author

Correspondence to Carlos A. Varela.

Appendices

Core PILOTS runtime library

The core PILOTS runtime library is in charge of starting a data receiving server, storing received data, providing data selection/interpolation service to the application, and sending processed data to output/error hosts.

Fig. 27
figure 27

Class diagram of PILOTS runtime library

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Primary classes included in the PILOTS runtime library shown in Fig. 27 are explained as follows.

  • PilotsRuntime class is extended by the application and provides all basic functions to run a PILOTS application other than application-specific processing. It starts DataReceiver to start receiving data, requests stored data from DataStore, and sends calculated outputs and errors to other hosts.

  • DataReceiver class receives data from data input clients from a port specified in the command-line arguments. Upon accepting data, it launches a new worker thread to receive data and the created thread requests to add these data to DataStore.

  • DataStore class accepts data from DataReceiver as a string, and then it asks SpatioTempoData to parse the string and stores the parsed data. It also implements getData() method supporting closest, euclidean, interpolate for data selection. When comparing locations and time for data selection/interpolation, it asks for the current time and location from CurrentLocationTimeService. Stored data are accessed from multiple threads (i.e., threads for adding data from DataReceiver vs. threads getting data from PilotsRuntime), so the data have to be protected from simultaneous data access.

  • CurrentLocationTimeService class is an interface class for providing the current time and location. Users have to implement this class for the system to work (e.g., SimpleTimeService and SimulationService). The implemented class can either return the actual current time for the real-time mode or past time for the simulation mode.

PILOTS with machine learning component grammar design

The grammar of the PILOTS programming language with machine learning component is designed as shown in Fig. 28. Also, the grammar of the PILOTS trainer file is shown in Fig. 29Footnote 3.

Fig. 28
figure 28

PILOTS with machine learning component grammar design

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Fig. 29
figure 29

PILOTS trainer file grammar design (Note: for the non-terminals not defined here, refer to Fig. 28)

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Example generated Java code

Generated Java code for the Twice PILOTS program (Fig. 1) is shown in Fig. 30. Also, the difference in generated Java code between Twice (Fig. 1) and Twice_Signatures (Fig. 4) PILOTS programs is shown in Fig. 31. Note that the difference is generated by the following Linux command:

$ diff Twice.java Twice_Signatures.java

Fig. 30
figure 30

Generated Java code for the Twice PILOTS program shown in Fig. 1

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Fig. 31
figure 31

Difference in generated Java code between Twice (Fig. 1) and Twice_Signatures (Fig. 4) PILOTS programs

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Imai, S., Chen, S., Zhu, W. et al. Dynamic data-driven learning for self-healing avionics. Cluster Comput 22 (Suppl 1), 2187–2210 (2019). https://doi.org/10.1007/s10586-017-1291-8

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