Skip to main content

Advertisement

SpringerLink
Log in

Conception and implementation of a data-driven prognostics algorithm for safety–critical systems

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Complex engineering systems suffer from internal wears and tears that cannot be measured by sensors. Sudden failure of such systems is hazardous and may endanger human life. To avoid sudden failures, a prognostics system that takes multivariate sensor data and infers system health and then projects the inferred system health into future based on damage progression for remaining useful life (RUL) estimation in real time is needed. Logisticians, engineers, project managers, and others can also benefit from prognostics information to improve performance and reduce cost. Our contribution in this paper is to present a data-driven prognostics approach for RUL estimation of aircraft turbofan engines that run onboard in real time. Kalman filter and neural network are used together to infer system health from several sensor readings. The inferred system health is then projected by another neural network till the end of life for RUL calculation. The algorithm is implemented on Raspberry Pi 2 single-board computer running Windows 10 Internet of Things Core to enable efficient development and deployment of onboard prognostics applications. Data from PHM08 data challenge competition are used for algorithm development and testing. The results show the applicability of this approach for RUL estimation onboard in real time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Alexandru S, Cristian P, Cristina Ş, Florin P (2017) Predicting provisioning and booting times in a metal-as-a-service system. Future Gener Comput Syst 72:180–192

    Article  Google Scholar 

  • Arduino (2005) https://www.arduino.cc/en/Guide/Introduction. Accessed 01 Dec 2015

  • Arduino (2005) https://www.arduino.cc/en/Reference/HomePage. Accessed 01 Dec 2015

  • Balaban E, Saxena A, Narasimhan S, Roychoudhury I, Koopmans M, Ott C, Goebel K (2015) Prognostic health-management system development for electromechanical actuators. J Aerosp Inf Syst 12(3):329–344. https://doi.org/10.2514/1.I010171

    Google Scholar 

  • Bond L (2008) Diagnostics and prognostics: state of the art and programs. PHM08 tutorial materials. IAEA Workshop, Argentina

  • Bonissone P (2006) Knowledge and time: a framework for soft computing applications in prognostics and health management (PHM). In: International symposium on evolving fuzzy systems, Ambleside, Cumbria, pp 19–24. https://doi.org/10.1109/ISEFS.2006.251159

  • Daeil K, Melinda R, Jiajie F, Tadahiro S, Michael G (2016) IoT-based prognostics and systems health management for industrial applications. IEEE Access 4:3659–3670. https://doi.org/10.1109/ACCESS.2016.2587754

    Article  Google Scholar 

  • Draper N, Smith H (1998) Applied regression analysis, 3rd edn. Wiley, New York

    Book  MATH  Google Scholar 

  • Elattar H (2011) Intelligent information system to forecast the remaining life of aircraft turbofan engine. MSc. thesis. Mansoura University, Mansoura, Egypt. http://www.eulc.edu.eg/eulc_v5/Libraries/Thesis/BrowseThesisPages.aspx?fn=PublicDrawThesis&BibID=379665

  • Evans D (2011) The internet of things how the next evolution of the internet is changing everything. White paper, Cisco

  • Feather M, Hicks K, Mackey R, Uckun S (2008) Guiding technology deployment decisions using a quantitative requirements analysis technique. In: 2008 IEEE international conference on requirements engineering, September 8–12, Barcelona, Spain

  • Goebel K, Qiu H, Eklund N, Yan W (2007) Modeling propagation of gas path damage. In: IEEE aerospace conference, Big Sky, MT. https://doi.org/10.1109/AERO.2007.352835

  • Heimes F (2008) Recurrent neural networks for remaining useful life estimation. In: 2008 international conference on prognostics and health management. October 6–9, Marriott Tech Center Denver, CO, USA. https://doi.org/10.1109/PHM.2008.4711422

  • Hu C, Youn BD, Wang P (2012) Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliab Eng Syst Saf 103:120–135

    Article  Google Scholar 

  • Kalman R (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82:35–45

    Article  Google Scholar 

  • Maybeck P (1979) Stochastic models, estimation and control, vol 1. Academic Press Inc, New York

    MATH  Google Scholar 

  • Medjahar K, Zerhouni N (2009) Residual-based failure prognostic in dynamic system. In: 7th IFAC international symposium on fault detection, supervision and safety of technical processes, June-30–July 3, Sants Hotel, Spain

  • Peel L (2008) Data driven prognostics using a Kalman filter ensemble of neural network models. In: 2008 international conference on prognostics and health management. October 6–9, Marriott Tech Center Denver, CO, USA. https://doi.org/10.1109/PHM.2008.4711423

  • Platzer P, Cappaert J (2012) ArduSat: your Arduino experiment in space. Summer CubeSat Developer’s workshop, Nanosatisfi LLC, August, p 17

  • Raspberry Pi (2015) https://www.raspberrypi.org/products/raspberry-pi-2-model-b/. Accessed 01 Dec 2015

  • Riad A, Elminir H, Elattar H (2010) Evaluation of neural networks in the subject of prognostics as compared to linear regression model. Int J Eng Technol 10:52–58

    Google Scholar 

  • Rouet V, Delye A, Vichare N, Pecht M, Foucher B (2007) Embedded remaining life prognostics and diagnostics of electronics. In: 1st international congress on microreliability and nanoreliability in key technology applications (micronanoreliability congress), September 2–5, Berlin, Germany

  • Saha S, Saha B, Saxena A, Goebel K (2010) Distributed prognostic health management with Gaussian process regression. In: 2010 IEEE aerospace conference, March 6-13, Big Sky, MT, pp 1 – 8. https://doi.org/10.1109/AERO.2010.5446841

  • Saxena A, Celaya J, Saha B, Saha S, Goebel K (2010) Metrics for offline evaluation of prognostic performance. Int J Progn Health Manag 1(1):001

    Google Scholar 

  • Saxena A, Balaban E, Goebel K, Saha B, Saha S, Schwabacher M (2008) Metrics for evaluating performance of prognostic technique. In: 2008 international conference on prognostics and health management, October 6–9, Marriott Tech Center Denver, CO, USA

  • Saxena A, Goebel K (2008) PHM08 challenge data set, NASA Ames prognostics data repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA

  • Saxena A, Goebel K, Simon D, Eklund N (2008) Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 international conference on prognostics and health management, pp 1–9, October 6-9, Marriott Tech Center Denver, CO, USA

  • Saxena A, Roychoudhury I, Celaya J, Saha S, Saha B, Goebel K (2010) Requirements specifications for prognostics: an overview. American institute of aeronautics and astronautics (AIAA). https://c3.nasa.gov/dashlink/resources/824/. Accessed 01 Dec 2015

  • Saxena A, Sankararaman S, Goebel K (2014) Performance evaluation for fleet-based and unit-based prognostic methods. In: Annual conference of the PHM society, fort worth, Texas, USA

  • Schwabacher M, Goebel K (2007) A survey of artificial intelligence for prognostics. In: Proceedings of AAAI fall symposium, November 9–11, Arlington, VA

  • Smith B (2011) Satellite enabled vehicle prognostic and diagnostic system. United States patent application publication US 2011/0046842 Al

  • Telecommunication Standardization sector of International Telecommunication Union (ITU-T) (2012) Overview of the Internet of things Y. 2060. Genève, Switzerland: International Telecommunication Union (ITU)

  • Vachtsevanos G, Lewis F, Roemer M, Hess A, Wu B (2006) Intelligent fault diagnosis and prognosis for engineering systems. Wiley, Hoboken

    Book  Google Scholar 

  • Voisin A, Levrat E, Cocheteux P, Iung B (2010) Generic prognosis model for proactive maintenance decision support: application to pre-industrial emaintenance test bed. J Intell Manuf 21(2):177–193

    Article  Google Scholar 

  • Wang P, Youn BD, Hu C (2012) A generic probabilistic framework for structural health prognostic and uncertainty management. Mech Syst Signal Process 28:622–637

    Article  Google Scholar 

  • Wang T, Lee J (2008) The operating regime approach for precision health prognosis. In: Proceedings of 62th meeting of the MFPT society: failure prevention for system availability, pp 87–98, May 6–8, Virginia Beach, VA, USA

  • Wang T, Yu J, Siegel D, Lee J (2008) A similarity-based prognostics approach for remaining useful life estimation of engineered systems. In: 2008 international conference on prognostics and health management. October 6–9, Marriott Tech Center Denver, CO, USA. https://doi.org/10.1109/PHM.2008.4711421

  • Welch G, Bishop G (2006) An introduction to the Kalman filter. UNC-Chapel Hill, TR 95-041, July 24

  • Wheeler K, Kurtoglu T, Poll S (2010) A survey of health management user objectives in aerospace systems related to diagnostic and prognostic metrics. Int J Progn Health Manag 1(1):003

    Google Scholar 

  • Yan J, Koc M, Lee J (2004) A prognostic algorithm for machine performance assessment and its application. Prod Plan Control 15(08):796–801

    Article  Google Scholar 

  • Zio E, Di Maio F (2010) A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear power plant. Reliab Eng Syst Saf 95(n1):49–57

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information System Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt

    Hatem M. Elattar & A. M. Riad

  2. Obour city, Cairo, Egypt

    Hatem M. Elattar

  3. Department of Electrical Engineering, Faculty of Engineering, Kafr Elshiekh University, Kafr Elshiekh, Egypt

    Hamdy K. Elminir

Authors
  1. Hatem M. Elattar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamdy K. Elminir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. M. Riad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hatem M. Elattar.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elattar, H.M., Elminir, H.K. & Riad, A.M. Conception and implementation of a data-driven prognostics algorithm for safety–critical systems. Soft Comput 23, 3365–3382 (2019). https://doi.org/10.1007/s00500-017-2995-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-017-2995-7

Keywords

Search

Navigation