Skip to main content

Advertisement

Log in

Recent trends and challenges in predictive maintenance of aircraft’s engine and hydraulic system

  • Review
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Predictive maintenance (PM) strategies are based on real-time data for diagnosis of impending failure and prognosis of machine health. It is a proactive process, which needs predictive modeling to trigger an alarm for maintenance activities and anticipate a failure before it occurs. Various industries have adopted PM techniques because of its advantage in increasing reliability and safety. But in the aviation industry, expectations for safety are increased due to its high cost and danger to human life when an aircraft fails or becomes out of service. Flight data monitoring systems are regularly implemented in commercial operations using artificial intelligence (AI) algorithms, but there is limited work specific to safety critical systems such as engine and hydraulic system. This paper provides a survey of recent work on PM of aircraft's’ hydraulic system and engine, identifying new trends and challenges. This work also highlights the importance of PM and state-of-the-art data pre-processing techniques for large datasets.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  1. Ahmadzadeh F, Lundberg J (2013) Application of multi regressive linear model and neural network for wear prediction of grinding mill liners. IJACSA 4(5)

  2. Albarbar A, Gu F, Ball A, Starr A (2010) Acoustic monitoring of engine fuel injection based on adaptive filtering techniques. Appl Acoust 71(12):1132–1141

    Google Scholar 

  3. Ali JB, Chebel-Morello B, Saidi L, Malinowski S, Fnaiech F (2015) Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mech Syst Signal Process 56:150–172

    Google Scholar 

  4. Analytics I (2017) Predictive maintenance market report 2017–22. Market Report. IoT Analytics, Hamburg, Germany

  5. Anderson JD Jr (2010) Fundamentals of aerodynamics. Tata McGraw-Hill Education, New York

    Google Scholar 

  6. Atamuradov V, Medjaher K, Dersin P, Lamoureux B, Zerhouni N (2017) Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation. Int J Prognos Health Manage 8(060):1–31

    Google Scholar 

  7. Babbar A, Syrmos VL, Ortiz EM, Arita MM (2009). Advanced diagnostics and prognostics for engine health monitoring. Paper presented at the 2009 IEEE aerospace conference

  8. Babel AS, Strangas EG (2014) Condition-based monitoring and prognostic health management of electric machine stator winding insulation. Paper presented at the 2014 international conference on electrical machines (ICEM)

  9. Badea VE, Zamfiroiu A, Boncea R (2018) Big data in the aerospace industry. Inform Econom 22(1):17–24

    Google Scholar 

  10. Banaee H, Ahmed MU, Loutfi A (2013) Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 13(12):17472–17500

    Google Scholar 

  11. Baptista M, de Medeiros IP, Malere JP, Prendinger H, Nascimento Jr CL, Henriques E (2016) A comparison of data-driven techniques for engine bleed valve prognostics using aircraft-derived fault messages. Paper presented at the third European conference of the prognostics and health management society

  12. Baraldi P, Compare M, Sauco S, Zio E (2013) Ensemble neural network-based particle filtering for prognostics. Mech Syst Signal Process 41(1–2):288–300

    Google Scholar 

  13. Batzel TD, Swanson DC (2009) Prognostic health management of aircraft power generators. IEEE Trans Aerosp Electron Syst 45(2):473–482

    Google Scholar 

  14. Bazovsky I (2004) Reliability theory and practice. Courier Corporation, North Chelmsford

    Google Scholar 

  15. Bock JR, Brotherton T, Grabill P, Gass D, Keller JA (2006) On false alarm mitigation. Paper presented at the 2006 IEEE aerospace conference

  16. Bonissone P, Hu X, Subbu R (2009) A systematic PHM approach for anomaly resolution: a hybrid neural fuzzy system for model construction. Paper presented at the annual conference of the PHM society

  17. Bonissone PP, Xue F, Subbu R (2011) Fast meta-models for local fusion of multiple predictive models. Appl Soft Comput 11(2):1529–1539

    Google Scholar 

  18. Bousdekis A, Magoutas B, Apostolou D, Mentzas G (2018) Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance. J Intell Manuf 29(6):1303–1316

    Google Scholar 

  19. Byington CS, Watson M, Edwards D, Dunkin B (2003) In-line health monitoring system for hydraulic pumps and motors. Paper presented at the 2003 IEEE aerospace conference proceedings (Cat. No. 03TH8652)

  20. Cadini F, Zio E, Avram D (2009) Model-based Monte Carlo state estimation for condition-based component replacement. Reliab Eng Syst Saf 94(3):752–758

    Google Scholar 

  21. Campolucci P, Uncini A, Piazza F, Rao BD (1999) On-line learning algorithms for locally recurrent neural networks. IEEE Trans Neural Netw 10(2):253–271

    Google Scholar 

  22. Carta S, Medda A, Pili A, Reforgiato Recupero D, Saia R (2019) Forecasting E-commerce products prices by combining an autoregressive integrated moving average (ARIMA) model and Google Trends data. Future Internet 11(1):5

    Google Scholar 

  23. Chehade A, Song C, Liu K, Saxena A, Zhang X (2018) A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes. J Qual Technol 50(2):150–165

    Google Scholar 

  24. Chen J, Ma C, Song D, Xu B (2016) Failure prognosis of multiple uncertainty system based on Kalman filter and its application to aircraft fuel system. Adv Mech Eng 8(10):1687814016671445

    Google Scholar 

  25. Clifton D, Tarassenko L (2006) Condition monitoring of gas-turbine engines. Transfer report, Department of Engineering Science, University of Oxford

  26. Coussement K, Lessmann S, Verstraeten G (2017) A comparative analysis of data preparation algorithms for customer churn prediction: a case study in the telecommunication industry. Decis Support Syst 95:27–36

    Google Scholar 

  27. Daigle MJ, Goebel K (2011) A model-based prognostics approach applied to pneumatic valves. Int J Prognos Health Manag 2(2):84–99

    Google Scholar 

  28. DePold HR, Gass FD (1998) The application of expert systems and neural networks to gas turbine prognostics and diagnostics. Paper presented at the turbo expo: power for land, sea, and air

  29. Di Maio F, Zio E (2013) Failure prognostics by a data-driven similarity-based approach. Int J Reliab Qual Saf Eng 20(01):1350001

    Google Scholar 

  30. Dixon MC (2006) The maintenance costs of aging aircraft: insights from commercial aviation, vol 486. Rand Corporation, Santa Monica

    Google Scholar 

  31. Dole CE, Lewis JE, Badick JR, Johnson BA (2016) Flight theory and aerodynamics: a practical guide for operational safety. Wiley, Hoboken

    Google Scholar 

  32. Du J, Wang S, Zhang H (2013) Layered clustering multi-fault diagnosis for hydraulic piston pump. Mech Syst Signal Process 36(2):487–504

    Google Scholar 

  33. Duesterhoeft W, Schulz MW, Clarke E (1951) Determination of instantaneous currents and voltages by means of alpha, beta, and zero components. Trans Am Inst Electr Eng 70(2):1248–1255

    Google Scholar 

  34. Eker ÖF, Camci F, Jennions IK (2012) Major challenges in prognostics: study on benchmarking prognostic datasets

  35. El-Betar A, Abdelhamed MM, El-Assal A, Abdelsatar R (2006) Fault diagnosis of a hydraulic power system using an artificial neural network. Eng Sci 17(1)

  36. Elangovan K, Krishnasamy Tamilselvam Y, Mohan RE, Iwase M, Takuma N, Wood KL (2017) Fault diagnosis of a reconfigurable crawling–rolling robot based on support vector machines. Appl Sci 7(10):1025

    Google Scholar 

  37. Elattar HM, Elminir HK, Riad AM (2016) Prognostics: a literature review. Complex Intell Syst 2(2):125–154. https://doi.org/10.1007/s40747-016-0019-3

    Article  Google Scholar 

  38. Eltoukhy AE, Chan FT, Chung SH (2017) Airline schedule planning: a review and future directions. Industrial Management & Data Systems

  39. Ethington JM, Sturlaugson LE, Schimert J, Wilmering TJ (2019) Predictive aircraft maintenance systems and methods incorporating classifier ensembles. In: Google Patents

  40. Faulstich S, Hahn B, Tavner PJ (2011) Wind turbine downtime and its importance for offshore deployment. Wind Energy 14(3):327–337

    Google Scholar 

  41. Ferreiro S, Arnaiz A, Sierra B, Irigoien I (2012) Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept. Expert Syst Appl 39(7):6402–6418

    Google Scholar 

  42. Fitch E (1979) An encyclopedia of fluid contamination control for hydraulic systems

  43. Forecast GM (2017) Forecast 2017–2036, Airbus, Growing horizons. In

  44. Frederick DK, DeCastro JA, Litt JS (2007) User's guide for the commercial modular aero-propulsion system simulation (C-MAPSS)

  45. Gao Y, Zhang Q (2006) A wavelet packet and residual analysis based method for hydraulic pump health diagnosis. Proc Inst Mech Eng Part D J Autom Eng 220(6):735–745

    Google Scholar 

  46. Gao Y, Zhang Q, Kong X (2003) Wavelet-based pressure analysis for hydraulic pump health diagnosis. Trans ASAE 46(4):969

    Google Scholar 

  47. Gavrilovski A, Jimenez H, Mavris DN, Rao AH, Shin S, Hwang I, Marais K (2016) Challenges and opportunities in flight data mining: a review of the state of the art. AIAA Infotech@ Aerospace, 0923.

  48. Gertsbakh I (2013) Reliability theory: with applications to preventive maintenance. Springer, Berlin

    MATH  Google Scholar 

  49. Gomes JPP, Leão BP, Vianna WO, Galvão RK, Yoneyama T (2012) Failure prognostics of a hydraulic pump using Kalman filter. Paper presented at the annual conference of the prognostics and health management society

  50. Gomes JPP, Rodrigues LR, Leão BP, Galvão RKH, Yoneyama T (2016) Using degradation messages to predict hydraulic system failures in a commercial aircraft. IEEE Trans Autom Sci Eng 15(1):214–224

    Google Scholar 

  51. Goupil P (2010) Oscillatory failure case detection in the A380 electrical flight control system by analytical redundancy. Control Eng Pract 18(9):1110–1119

    Google Scholar 

  52. Gu J, Zhang G, Li KW (2015) Efficient aircraft spare parts inventory management under demand uncertainty. J Air Transp Manag 42:101–109

    Google Scholar 

  53. Hayton P, Utete S, King D, King S, Anuzis P, Tarassenko L (2007) Static and dynamic novelty detection methods for jet engine health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365(1851):493–514

    Google Scholar 

  54. He W, Williard N, Chen C, Pecht M (2014) State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation. Int J Electr Power Energy Syst 62:783–791

    Google Scholar 

  55. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    MathSciNet  MATH  Google Scholar 

  56. Huang G, Huang G-B, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48

    MATH  Google Scholar 

  57. Illman PE (1995) The pilot's handbook of aeronautical knowledge. TAB Books

  58. Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510

    Google Scholar 

  59. Jaw LC (2005) Recent advancements in aircraft engine health management (EHM) technologies and recommendations for the next step. Paper presented at the turbo expo: power for land, sea, and air

  60. Jegadeeshwaran R, Sugumaran V (2015) Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines. Mech Syst Signal Process 52:436–446

    Google Scholar 

  61. Jia F, Lei Y, Guo L, Lin J, Xing S (2018) A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines. Neurocomputing 272:619–628

    Google Scholar 

  62. Jian X, Dong F, Xu J, Li Z, Jiao Y, Cui Y (2018) Frequency domain analysis of multiwavelength photoacoustic signals for differentiating tissue components. Int J Thermophys 39(5):58

    Google Scholar 

  63. Jianzhong S, Fangyuan W, Shungang NJCJoA (2020) Aircraft air conditioning system health state estimation and prediction for predictive maintenance. 33(3):947–955

  64. Jun H-B, Kim D (2017) A Bayesian network-based approach for fault analysis. Expert Syst Appl 81:332–348

    Google Scholar 

  65. Korvesis P, Besseau S, Vazirgiannis M (2018) Predictive maintenance in aviation: Failure prediction from post-flight reports. Paper presented at the 2018 IEEE 34th international conference on data engineering (ICDE)

  66. Lampis M, Andrews J (2009) Bayesian belief networks for system fault diagnostics. Qual Reliab Eng Int 25(4):409–426

    Google Scholar 

  67. Le Son K, Fouladirad M, Barros A, Levrat E, Iung B (2013) Remaining useful life estimation based on stochastic deterioration models: a comparative study. Reliab Eng Syst Saf 112:165–175

    Google Scholar 

  68. Lei Y, Lin J, He Z, Zuo MJ (2013) A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process 35(1–2):108–126

    Google Scholar 

  69. Létourneau S, Famili F, Matwin S (1999) Data mining to predict aircraft component replacement. IEEE Intell Syst Appl 14(6):59–66

    Google Scholar 

  70. Li S, Zhang G, Wang J (2017) Civil aircraft health management research based on big data and deep learning technologies. Paper presented at the 2017 IEEE international conference on prognostics and health management (ICPHM)

  71. Li Y, Kurfess T, Liang S (2000) Stochastic prognostics for rolling element bearings. Mech Syst Signal Process 14(5):747–762

    Google Scholar 

  72. Lin XS, Li BW, Yang XY (2016) Engine components fault diagnosis using an improved method of deep belief networks. Paper presented at the 2016 7th international conference on mechanical and aerospace engineering (ICMAE)

  73. Lin Z, Zheng G, Wang J, Shen Y, Chu B (2016) The method for identifying the health state of aircraft hydraulic pump based on grey prediction. Paper presented at the 2016 prognostics and system health management conference (PHM-Chengdu)

  74. Litt JS, Simon DL, Garg S, Guo T-H, Mercer C, Millar R, Behbahani A, Bajwa A, Jensen DT (2004) A survey of intelligent control and health management technologies for aircraft propulsion systems. J Aerosp Comput Inf Commun 1(12):543–563

    Google Scholar 

  75. Liu K, Feng Y, Xue X (2017) Fault diagnosis and health assessment of landing gear hydraulic retraction system based on multi-source information feature fusion. Paper presented at the 2017 international conference on sensing, diagnostics, prognostics, and control (SDPC)

  76. Lowe D, Tipping M (1996) Feed-forward neural networks and topographic mappings for exploratory data analysis. Neural Comput Appl 4(2):83–95

    Google Scholar 

  77. Lu J, Huang J, Lu F (2017) Sensor fault diagnosis for aero engine based on online sequential extreme learning machine with memory principle. Energies 10(1):39

    Google Scholar 

  78. Lu P-J, Zhang M-C, Hsu T-C, Zhang J (2001) An evaluation of engine faults diagnostics using artificial neural networks. J Eng Gas Turbines Power 123(2):340–346

    Google Scholar 

  79. Lv Z, Wang J, Zhang G, Jiayang H (2015) Prognostics health management of condition-based maintenance for aircraft engine systems. Paper presented at the 2015 IEEE conference on prognostics and health management (PHM)

  80. Ma J, Su H, Zhao W-l, Liu B (2018) Predicting the remaining useful life of an aircraft engine using a stacked sparse autoencoder with multilayer self-learning. Complexity 2018

  81. Mahantesh N, Aditya P, Kumar U (2014) Integrated machine health monitoring: a knowledge based approach. Int J Syst Assur Eng Manag 5(3):371–382

    Google Scholar 

  82. Marinai L, Probert D, Singh R (2004) Prospects for aero gas-turbine diagnostics: a review. Appl Energy 79(1):109–126

    Google Scholar 

  83. Marzat J, Piet-Lahanier H, Damongeot F, Walter E (2012) Model-based fault diagnosis for aerospace systems: a survey. Proc Inst Mech Eng Part G J Aerosp Eng 226(10):1329–1360

    Google Scholar 

  84. Mobley RK (2002) An introduction to predictive maintenance. Elsevier, Amterdam

    Google Scholar 

  85. Moir I, Seabridge A (2011) Aircraft systems: mechanical, electrical, and avionics subsystems integration, vol 52. Wiley, Hoboken

    Google Scholar 

  86. Montgomery DC (2007) Introduction to statistical quality control. Wiley, Hoboken

    MATH  Google Scholar 

  87. Mosallam A, Medjaher K, Zerhouni N (2016) Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. J Intell Manuf 27(5):1037–1048

    Google Scholar 

  88. Müller M, Falk E, Meira JA, Sassioui R, Sate R (2020) Predicting failures in 747–8 aircraft hydraulic pump systems. Paper presented at the 2020 IEEE aerospace conference

  89. Nayyeri S (2013) Aircraft jet engine condition monitoring through system identification by using genetic programming. Concordia University

  90. Nieto PG, García-Gonzalo E, Lasheras FS, de Cos Juez FJ (2015) Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliab Eng Syst Saf 138:219–231

    Google Scholar 

  91. Ning S, Sun J, Liu C, Yi YJPotIoME, Part O: Journal of Risk, & Reliability (2021) Applications of deep learning in big data analytics for aircraft complex system anomaly detection. 1748006X211001979

  92. Niu G, Yang B-S (2009) Dempster-Shafer regression for multi-step-ahead time-series prediction towards data-driven machinery prognosis. Mech Syst Signal Process 23(3):740–751

    Google Scholar 

  93. Oddan H (2017) Multivariate statistical condition monitoring. NTNU

  94. Oppenheimer CH, Loparo KA (2002) Physically based diagnosis and prognosis of cracked rotor shafts. Paper presented at the component and systems diagnostics, prognostics, and health management II

  95. Orchard ME, Vachtsevanos GJ (2009) A particle-filtering approach for on-line fault diagnosis and failure prognosis. Trans Inst Meas Control 31(3–4):221–246

    Google Scholar 

  96. Pecht M, Kapur KC, Kang R, Zhang S (2011) Foundations of reliability engineering

  97. Peel L (2008) Data driven prognostics using a Kalman filter ensemble of neural network models. Paper presented at the 2008 international conference on prognostics and health management

  98. Perez PL, Boehman AL (2010) Performance of a single-cylinder diesel engine using oxygen-enriched intake air at simulated high-altitude conditions. Aerosp Sci Technol 14(2):83–94

    Google Scholar 

  99. Petit-Renaud S, Denœux T (2004) Nonparametric regression analysis of uncertain and imprecise data using belief functions. Int J Approx Reason 35(1):1–28

    MathSciNet  MATH  Google Scholar 

  100. Pingchao LHWSO (2007) Fault diagnosis based on wavelet package and Elman neural network for a hydraulic pump. J Beijing Univ Aeronaut Astronaut 1

  101. Pitta S, Rojas J, Crespo D (2017) Comparison of fatigue crack growth of riveted and bonded aircraft lap joints made of Aluminium alloy 2024-T3 substrates—a numerical study. Paper presented at the Journal of Physics: Conference Series, Volume 843, conference 1

  102. Plakandaras V, Gogas P, Papadimitriou T (2019) The effects of geopolitical uncertainty in forecasting financial markets: a machine learning approach. Algorithms 12(1):1

    MathSciNet  MATH  Google Scholar 

  103. Qin W-L, Zhang W-J, Lu C (2016) A method for aileron actuator fault diagnosis based on PCA and PGC-SVM. Shock Vib 2016

  104. Rabenoro T, Lacaille J, Cottrell M, Rossi F (2015) Interpretable aircraft engine diagnostic via expert indicator aggregation. arXiv preprint 1503.05526

  105. Ratner B (2017) Statistical and machine-learning data mining: techniques for better predictive modeling and analysis of big data. CRC Press, Boca Raton

    MATH  Google Scholar 

  106. Sadough Vanini Z, Meskin N, Khorasani K (2014) Multiple-model sensor and components fault diagnosis in gas turbine engines using autoassociative neural networks. J Eng Gas Turbines And ower 136(9)

  107. Salfner F, Lenk M, Malek M (2010) A survey of online failure prediction methods. ACM Comput Surv (CSUR) 42(3):1–42

    Google Scholar 

  108. Saxena A, Goebel K, Simon D, Eklund N (2008) Damage propagation modeling for aircraft engine run-to-failure simulation. Paper presented at the 2008 international conference on prognostics and health management

  109. Schneider T, Helwig N, Klein S, Schütze A (2018) Influence of sensor network sampling rate on multivariate statistical condition monitoring of industrial machines and processes. Paper presented at the multidisciplinary digital publishing institute proceedings

  110. Schwabacher M, Goebel K (2007) A survey of artificial intelligence for prognostics. Paper presented at the AAAI fall symposium: artificial intelligence for prognostics

  111. Selcuk S (2017) Predictive maintenance, its implementation and latest trends. Proc Inst Mech Eng Part B J Eng Manuf 231(9):1670–1679

    Google Scholar 

  112. Shao-ping W, Qian-xia M, Chuan-qi L (2015) State recognition based on wavelet packet norm entropy for aircraft hydraulic pump. Paper presented at the 2015 international conference on fluid power and mechatronics (FPM)

  113. Shao Y, Nezu K (2000) Prognosis of remaining bearing life using neural networks. Proc Inst Mech Eng Part I J Syst Control Eng 214(3):217–230

    Google Scholar 

  114. Sikorska J, Hodkiewicz M, Ma L (2011) Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process 25(5):1803–1836

    Google Scholar 

  115. Sotiris VA, Pecht MG (2007) Support vector prognostics analysis of electronic products and systems. Paper presented at the AAAI fall symposium: artificial intelligence for prognostics

  116. Sridhar S, Rao KU, Umesh R, Harish K (2016) Condition monitoring of Induction Motor using statistical processing. Paper presented at the 2016 IEEE region 10 conference (TENCON)

  117. Strączkiewicz M, Klepka A, Staszewski WJ, Aymerich F (2014) Triple correlation technique for damage detection in composite materials. Paper presented at the key engineering materials

  118. Sun J, Li H, Xu B (2016) Prognostic for hydraulic pump based upon DCT-composite spectrum and the modified echo state network. Springerplus 5(1):1–17

    Google Scholar 

  119. Susto GA, Schirru A, Pampuri S, McLoone S, Beghi A (2014) Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans Industr Inf 11(3):812–820

    Google Scholar 

  120. Swędrowski L, Duzinkiewicz K, Grochowski M, Rutkowski T (2014) Use of neural networks in diagnostics of rolling-element bearing of the induction motor. Paper presented at the key engineering materials

  121. Tayarani-Bathaie SS, Vanini ZS, Khorasani K (2014) Dynamic neural network-based fault diagnosis of gas turbine engines. Neurocomputing 125:153–165

    Google Scholar 

  122. Tobon-Mejia DA, Medjaher K, Zerhouni N, Tripot G (2011) Hidden Markov models for failure diagnostic and prognostic. Paper presented at the 2011 prognostics and system health managment confernece

  123. Tongyang L, Shaoping W, Jian S, Zhonghai M (2018) An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps. Chin J Aeronaut 31(5):941–948

    Google Scholar 

  124. Uday P, Ganguli R (2010) Jet engine health signal denoising using optimally weighted recursive median filters. J Eng Gas Turbines Power 132(4)

  125. Vachtsevanos GZ (2006) Intelligent fault diagnosis and prognosis for engineering systems. Wiley, Hoboken

    Google Scholar 

  126. Vanini ZS, Khorasani K, Meskin N (2014) Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach. Inf Sci 259:234–251

    Google Scholar 

  127. Vianna WO, Gomes JP, Galvão RK, Yoneyama T, Matsuura JP (2011) Health monitoring of an auxiliary power unit using a classification tree. Paper presented at the proceedings of annual conference of the prognostics and health management society

  128. Vianna WOL, Yoneyama T (2017) Predictive maintenance optimization for aircraft redundant systems subjected to multiple wear profiles. IEEE Syst J 12(2):1170–1181

    Google Scholar 

  129. Volponi AJ, DePold H, Ganguli R, Daguang C (2003) The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study. J Eng Gas Turbines Power 125(4):917–924

    Google Scholar 

  130. Walton P (2018) Artificial intelligence and the limitations of information. Information 9(12):332

    Google Scholar 

  131. Wang K-S, Sharma VS, Zhang Z-Y (2014) SCADA data based condition monitoring of wind turbines. Adv Manuf 2(1):61–69

    Google Scholar 

  132. Wang WQ, Golnaraghi MF, Ismail F (2004) Prognosis of machine health condition using neuro-fuzzy systems. Mech Syst Signal Process 18(4):813–831

    Google Scholar 

  133. Wang X, Lin S, Wang S (2016) Remaining useful life prediction model based on contaminant sensitivity for aviation hydraulic piston pump. Paper presented at the 2016 IEEE international conference on aircraft utility systems (AUS)

  134. Wang Y, Deng C, Wu J, Wang Y, Xiong Y (2014) A corrective maintenance scheme for engineering equipment. Eng Fail Anal 36:269–283

    Google Scholar 

  135. Wang Y, Kung L, Wang WYC, Cegielski CG (2018) An integrated big data analytics-enabled transformation model: application to health care. Inf Manage 55(1):64–79

    Google Scholar 

  136. Wang Y, Ma Q, Zhu Q, Liu X, Zhao L (2014) An intelligent approach for engine fault diagnosis based on Hilbert-Huang transform and support vector machine. Appl Acoust 75:1–9

    Google Scholar 

  137. Wang Z, Lu C, Wang Z (2014) Chaotic information-geometric support vector machine and its application to fault diagnosis of hydraulic pumps. J Vibroeng 16(2):1033–1041

    Google Scholar 

  138. Wu S-D, Wu P-H, Wu C-W, Ding J-J, Wang C-C (2012) Bearing fault diagnosis based on multiscale permutation entropy and support vector machine. Entropy 14(8):1343–1356

    MATH  Google Scholar 

  139. Xu J, Wang Y, Xu L (2013) PHM-oriented integrated fusion prognostics for aircraft engines based on sensor data. IEEE Sens J 14(4):1124–1132

    Google Scholar 

  140. Yan H, Sun J, Zuo HJPotIoME., Part I: Journal of Systems, & Engineering, C (2020) Anomaly detection based on multivariate data for the aircraft hydraulic system. 0959651820954577

  141. Yan H, Zuo H, Sun J, Gao N, Wang F (2019) Research on anomaly detection of civil aircraft hydraulic system based on multivariate monitoring data. Paper presented at the 2019 IEEE AUTOTESTCON

  142. Yan X, Zheng L (2017) Fundamental analysis and the cross-section of stock returns: a data-mining approach. Rev Financ Stud 30(4):1382–1423

    Google Scholar 

  143. Yang J, Zhang Y, Zhu Y (2007) Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension. Mech Syst Signal Process 21(5):2012–2024

    Google Scholar 

  144. Yang S-B, Wang L-B, Xu D (2018) Computational analysis on actuator failures of flexible aircraft. Int J Comput Mat Sci Eng 7(01–02):1850014

    Google Scholar 

  145. Yang X, Pang S, Shen W, Lin X, Jiang K, Wang Y (2016) Aero engine fault diagnosis using an optimized extreme learning machine. Int J Aerosp Eng 2016

  146. You C-X, Huang J-Q, Lu F (2016) Recursive reduced kernel based extreme learning machine for aero-engine fault pattern recognition. Neurocomputing 214:1038–1045

    Google Scholar 

  147. You M-Y, Liu F, Wang W, Meng G (2010) Statistically planned and individually improved predictive maintenance management for continuously monitored degrading systems. IEEE Trans Reliab 59(4):744–753

    Google Scholar 

  148. Yu X, Jiang J (2015) A survey of fault-tolerant controllers based on safety-related issues. Annu Rev Control 39:46–57

    Google Scholar 

  149. Zeldam S (2018) Automated failure diagnosis in aviation maintenance using explainable artificial intelligence (XAI). University of Twente

  150. Zhang C, Wang N (2012) Aero-engine condition monitoring based on support vector machine. Phys Procedia 24:1546–1552

    Google Scholar 

  151. Zhang P, Huang J-Q (2008) SRUKF research on aeroengines for gas path component fault diagnostics. J Aerosp Power 23(1):169–173

    Google Scholar 

  152. Zhang S, Mathew J, Ma L, Sun Y, Mathew AD (2004) Statistical condition monitoring based on vibration signals

  153. Zhanlin W (2000) Trends of future aircraft hydraulic system. Hydraul Pnenm Seals 1

  154. Zhaomin H, Shaoping W (2014) Wear status recognition of piston pump based on side frequency relative energy summation. J Beijing Univ Aeronaut Astronaut 2:8

    Google Scholar 

  155. Zitzler E, Künzli S (2004) Indicator-based selection in multiobjective search. Paper presented at the international conference on parallel problem solving from nature

Download references

Acknowledgements

The authors would like to thank and acknowledge the Higher Education Commission Pakistan for funding this work through the Grant TDF 03-054. This funding (TDF 03-054) was awarded to Dr. Tanvir Ahmad (PI) and Dr. Abdul Basit (Co-PI).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Khalid Khan.

Additional information

Technical Editor: Monica Carvalho.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, K., Sohaib, M., Rashid, A. et al. Recent trends and challenges in predictive maintenance of aircraft’s engine and hydraulic system. J Braz. Soc. Mech. Sci. Eng. 43, 403 (2021). https://doi.org/10.1007/s40430-021-03121-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-021-03121-2

Keywords

Navigation