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Greener aviation with virtual sensors: a case study

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Abstract

The environmental impact of aviation is enormous given the fact that in the US alone there are nearly 6 million flights per year of commercial aircraft. This situation has driven numerous policy and procedural measures to help develop environmentally friendly technologies which are safe and affordable and reduce the environmental impact of aviation. However, many of these technologies require significant initial investment in newer aircraft fleets and modifications to existing regulations which are both long and costly enterprises. We propose to use an anomaly detection method based on Virtual Sensors to help detect overconsumption of fuel in aircraft which relies only on the data recorded during flight of most existing commercial aircraft, thus significantly reducing the cost and complexity of implementing this method. The Virtual Sensors developed here are ensemble-learning regression models for detecting the overconsumption of fuel based on instantaneous measurements of the aircraft state. This approach requires no additional information about standard operating procedures or other encoded domain knowledge. We present experimental results on three data sets and compare five different Virtual Sensors algorithms. The first two data sets are publicly available and consist of a simulated data set from a flight simulator and a real-world turbine disk. We show the ability to detect anomalies with high accuracy on these data sets. These sets contain seeded faults, meaning that they have been deliberately injected into the system. The second data set is from real-world fleet of 84 jet aircraft where we show the ability to detect fuel overconsumption which can have a significant environmental and economic impact. To the best of our knowledge, this is the first study of its kind in the aviation domain.

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References

  • Abdul-Aziz A, Woike M, Oza N, Matthews B, Baakilini G (2010) Propulsion health monitoring of a turbine engine disk using spin test data. Smart structures and materials and nondestructive evaluation and health monitoring

  • Breiman L: Bagging predictors. Mach Learn 24(2), 123–140 (1996)

    MATH  MathSciNet  Google Scholar 

  • Chandola V, Vatsavai R (2010) Scalable hyper-parameter estimation for Gaussian process based time series analysis. In: Proceedings of the SIGKDD workshop on large-scale data mining, Washington DC

  • Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3)

  • Chen T, Ren J: Bagging for Gaussian process regression. Neurocomputing 72(7–9), 1605–1610 (2009)

    Article  Google Scholar 

  • Chu E, Gorinevsky D, Boyd SP (2010) Detecting aircraft performance anomalies from cruise flight data. In: Proceedings of the AIAA infotech aerospace conference, Atlanta, GA

  • Collobert R, Bengio S: SVMTorch: support vector machines for large-scale regression problems. J Mach Learn Res 1, 143–160 (2001)

    MathSciNet  Google Scholar 

  • Committee on Aeronautics Research and Technology for Environmental Compatibility (2002) For greener skies: reducing environmental impacts of aviation. Tech report, National Academies Press

  • Foster L, Waagen A, Aijaz N, Hurley M, Luis A, Rinsky J, Satyavolu C, Way M, Gazis P, Srivastava AN: Stable and efficient Gaussian process calculations. J Mach Learn Res 10, 857–882 (2009)

    MathSciNet  Google Scholar 

  • Friedman J, Hastie T, Tibshirani R: Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33(1), 1–22 (2010)

    Google Scholar 

  • Friedman J, Hastie T, Höfling H, Tibshirani R: Pathwise coordinate optimization. Annals Appl Stat 1, 302–332 (2007)

    Article  MATH  Google Scholar 

  • Hastie T, Tibshirani R, Friedman J: The elements of statistical learning: data mining, inference, and prediction. Springer, New York (2001)

    MATH  Google Scholar 

  • Hastie T, Friedman JH, Tibshirani R: Glmnet for Matlab. Technical report, Stanford University (2009)

    Google Scholar 

  • Litt JS, Frederick DK, DeCastro JA (2007) User’s guide for the commercial modular aero-propulsion system simulation (c-mapps). NASA technical report

  • Matlab Statistics Toolbox (2010) Ver. 7.4, The MathWorks, Inc., Natick

  • Matthews B, Srivastava AN (2010) Adaptive fault detection on liquid propulsion systems with virtual sensors: algorithms and case study. In: Proceedings of the joint army navy NASA air force conference on propulsion

  • Nabney IT: NETLAB-algorithms for pattern recognition. Springer, New York (2001)

    Google Scholar 

  • Oza N (2010) FLTz simulated data. https://c3.ndc.nasa.gov/dashlink/resources/142

  • Penner JE, Lister DH, Griggs DJ, Dokken DJ, McFarland M (eds) (1999) Global aviation and the atmosphere. Technical report, Intergovernmental Panel on Climate Change

  • Petrovskaya A, Thrun S: Model based vehicle detection and tracking for autonomous urban driving. Autonomous robots. Springer, NEW York (2009)

    Google Scholar 

  • Rasmussen CE, Williams CKI: Gaussian processes for machine learning. MIT Press, Cambridge (2006)

    MATH  Google Scholar 

  • Sallee GP (1980) Performance deterioration based on in-service engine data. NASA technical manuscript CR-159525

  • Seber GAF, Wild CJ: Nonlinear regression. Wiley, New York (1989)

    Book  MATH  Google Scholar 

  • Simon DL (2010) A three-dimensional receiver operator characteristic surface diagnostic metric. IN: Annual conference of the prognostics and health management society

  • Srivastava AN, Das S (2009) Detection and prognostics on low dimensional systems. IEEE Trans Syst Man Cybernet C 39(1)

  • Srivastava AN, Oza NC, Stroeve J (2005) Virtual sensors: using data mining techniques to efficiently estimate remote sensing spectra. IEEE Transactions on Geoscience and Remote Sensing 43(3)

  • Statler IC (2007) The aviation system monitoring and modeling (ASMM) project: a documentation of its history and accomplishments: 1999 to 2005. NASA technical publication TP-2007-214556

  • Way MJ, Srivastava AN: Novel methods for predicting photometric redshifts from broad-band photometry using virtual sensors. Astrophys J 647, 102–115 (2006)

    Article  Google Scholar 

  • Wulf R (1980) Engine diagnostics program: Cf-60 engine deterioration. NASA technical report CR-159867

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Authors and Affiliations

  1. Intelligent Systems Division, Intelligent Data Understanding Group, NASA Ames Research Center, Moffett Field, CA, 94035, USA

    Ashok N. Srivastava

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  1. Ashok N. Srivastava
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Correspondence to Ashok N. Srivastava.

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Responsible editor: Katharina Morik, Kanishka Bhaduri and Hillol Kargupta.

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Srivastava, A.N. Greener aviation with virtual sensors: a case study. Data Min Knowl Disc 24, 443–471 (2012). https://doi.org/10.1007/s10618-011-0240-z

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  • DOI: https://doi.org/10.1007/s10618-011-0240-z

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