Efficient anomaly classification for spacecraft reaction wheels

  • Ehab A. Omran
  • Wael A. Murtada
Original Article


Attitude Determination and Control Subsystem (ADCS) in spacecraft is one of the vital systems for low-earth-orbit spacecraft in which the pointing accuracy is a highly recommended factor to satisfy its mission requirements. When any malfunction takes place at attitude actuator, the spacecraft will not satisfy the required mission objectives. It is required to identify specific faults for attitude actuators in a manner that helps to optimally recover this type of anomaly. An efficient anomaly detection and identification technique is a suitable way to identify such anomaly. This research introduces an accurate and high-performance methodology for fault detection and identification for spacecraft reaction wheels (RW) as (ADCS) actuator. Proposed approach is to differentiate among signatures of possible anomalies that may be occurred at RW like under-voltage, over-voltage, current losses, temperature decrease, and temperature increase. “Prony method,” as a feature extraction technique, is used to discriminate between normal system behavior and anomalies based on the three-axis spacecraft RW operation. A feed-forward neural network with back-propagation algorithm is used for anomaly identification. Prony order assessment is also carried out to obtain the proper order of poles and zeros to minimize the processing time required for identification. The results verify that the proposed anomaly identification is successfully accomplished with high degree of confidence and with minimal execution time. Research approach leads to generic methodology for anomaly identification process among all spacecraft subsystem anomalies.


Spacecraft anomaly detection Spacecraft anomaly identification Prony feature extraction Artificial neural network 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Ure N, Kaya Y, Inalhan G, (2011) The development of a software and hardware in the-loop test system for ITU-PSAT nano satellite ADCS. In: IEEE aerospace conference, vol 16. pp 1–15Google Scholar
  2. 2.
    Boskovic JD, Li SM, Mehra RK (1999) Intelligent control of spacecraft in the presence of actuator failures, In: 38th IEEE conference on decision and control, vol 5. IEEE, 6:4472–4477Google Scholar
  3. 3.
    Pirmoradi F, Sassani F, De Silva C (2009) Fault detection and diagnosis in a spacecraft ADCS. Acta Astronaut 65(5):710–729CrossRefGoogle Scholar
  4. 4.
    Venkatasubramanian V et al (2003) A review of process fault detection and diagnosis part I: Quantitative model-based methods. Comput Chem Eng 27(19):293–311CrossRefGoogle Scholar
  5. 5.
    Venkatasubramanian V et al (2003) A review of process fault detection and diagnosis part II: Qualitative models and search strategies. Comput Chem Eng 27(14):313–326CrossRefGoogle Scholar
  6. 6.
    Venkatasubramanian V et al (2003) A review of process fault detection and diagnosis part III: Process history based methods. Comput Chem Eng 27(20):327–346CrossRefGoogle Scholar
  7. 7.
    Al-Zyoud IAD, Khorasani K (2005) Detection of actuator faults using a dynamic neural network for the attitude control subsystem of a satellite. In: Conference on neural networks, Montreal, Canada, vol 5. pp 1747–1751Google Scholar
  8. 8.
    Wu Q, Saif M (2005) Neural adaptive observer based fault detection and identification for satellite attitude control systems. In: 2005 American control conference, USA, vol 5. pp 1055–1059Google Scholar
  9. 9.
    Li ZQ, Ma L, Khorasani K (2006) A dynamic neural network-based reaction wheel fault diagnosis for satellites. In: International joint conference on neural networks, vol 8. Vancouver, BC, Canada, pp 3714–3721Google Scholar
  10. 10.
    Talebi HA, Patel RV (2006) An intelligent fault detection and recovery scheme for reaction wheel actuator of satellite attitude control systems. In: IEEE international conference on control applications Munich, Germany, vol 6. pp 3282–3287Google Scholar
  11. 11.
    Selmic RR, Polycarpou Marios M, Parisin T (2009) Actuator fault detection in nonlinear uncertain systems using neural on-line approximation models. Eur J Control EUCA No. 1 16:29–44MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Wu Q, Saif M (2009) Model-based robust fault diagnosis for satellite control systems using learning and sliding mode approaches. J Comput 4(10):1022–1032CrossRefGoogle Scholar
  13. 13.
    Baldi P, Castaldi P, et al (2010) Fault diagnosis and control reconfiguration for satellite reaction wheels. In: Conference on control and fault tolerant systems, France, vol 6. pp 143–148Google Scholar
  14. 14.
    Long X et al (2011) Anomaly detection of spacecraft based on least squares support vector machine. In: Prognostics and system health management conference, Shenzhen, vol 6. pp 1–6Google Scholar
  15. 15.
    Regaieg M et al (2013) Fault detection and isolation of spacecraft thrusters by using principal component analysis. In: 4th European conference for aerospace sciences, Algiers, vol 6. pp 1–6Google Scholar
  16. 16.
    Odendaal HM, Jones Th (2014) “Actuator fault detection and isolation: an optimised parity space approach. ELSEVIER Control Eng Pract 26:222–232CrossRefGoogle Scholar
  17. 17.
    Gueddi I, Nasri O, Benothman K, Dague P (2015) VPCA-based fault diagnosis of spacecraft reaction wheels. In: XXV conference on information, communication, and automation technology, Sarajevo, vol 6. pp 1–6Google Scholar
  18. 18.
    Gao Y et al (2012) Fault detection and diagnosis for spacecraft using principal component analysis and support vector machines. In: 7th IEEE conference on industrial electronics and applications (ICIEA), Singapore, vol 5. pp 1984–1988Google Scholar
  19. 19.
    Baldi P et al (2016) Combined geometric and neural network approach to generic fault diagnosis in satellite actuators and sensors. In: 20th IFAC symposium on automatic control in aerospace sherbrooke, Quebec, Canada, vol 49(No. 17), 6, pp 432–437Google Scholar
  20. 20.
    Mousavi S, Khorasani K (2014) Fault detection of reaction wheels in attitude control subsystem of formation flying satellites. Int J Intell Unmanned Syst 2(1):2–26CrossRefGoogle Scholar
  21. 21.
    Froelich R, Papapoff H (1959) Reaction wheel attitude control for space vehicles. Autom Control IRE Trans 4:139–149CrossRefGoogle Scholar
  22. 22.
    Bialke B (1998) High fidelity mathematical modeling of reaction wheel performance. In: 21th annual American astronautical society guidance and control conference, vol 14. pp 483–496Google Scholar
  23. 23.
    Tawfik MM, Morcos MM (2005) On the use of Prony method to locate faults in loop systems by utilizing modal parameters of fault current. IEEE Power Deliv Trans 20(1):532–534CrossRefGoogle Scholar
  24. 24.
    Moustafa A, et al (2009) Electrocardiogram signals identification for cardiac arrhythmias using Prony’s method and neural network. In: 31st international conference of the IEEE, Minnesota, USA, vol 4. pp 893–1896Google Scholar
  25. 25.
    Farshad M, Sadeh J (2014) Transmission line fault location using hybrid wavelet-Prony method and relief algorithm. Electr Power Energy Syst 61(10):127–136CrossRefGoogle Scholar
  26. 26.
    Elsayed OA, Eldeib A, Elhefnawi F (2014) Parametric modeling of ICTAL Epilepsy EEG signal using Prony method. Int J Comput Sci Softw Eng (IJCSSE) 3(1):86–89Google Scholar
  27. 27.
    Faiz J, Lotfi-fard S (2007) Prony-Based Optimal Bayes Fault Classification of Overcurrent Protection. IEEE Trans Power Deliv 22(3):1326–1334CrossRefGoogle Scholar
  28. 28.
    Reid HM (2013) Introduction to statistics: fundamental concepts and procedures of data analysis. 1st edn. ISBN: 978-1452271965Google Scholar
  29. 29.
    Fausett LV (1993) Fundamentals of neural networks: architectures, algorithms and applications. 1st edn. ISBN: 9780133341867Google Scholar
  30. 30.
    Zurada JM (1992) Introduction to artificial neural systems. West Publishing Comp, St. Paul. ISBN 0-314-93391-3Google Scholar
  31. 31.
    Kolcio Ksenia (2016) Model-based fault detection and identification system for increased autonomy. Am Inst Aeronaut Astronaut AIAA 12:1–12Google Scholar
  32. 32.
    Omran EA, Murtada WA (2017) Fault detection and identification of spacecraft reaction wheels using autoregressive moving average model and neural networks. In: IEEE 12th international computer engineering conference (ICENCO), vol 5. pp 77–82Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Egyptian Space Technology Center (STC/FUE)CairoEgypt
  2. 2.National Authority for Remote Sensing and Space Sciences (NARSS)CairoEgypt

Personalised recommendations