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Predictive Maintenance in the Military Domain: A Systematic Review of the Literature

Published:13 July 2023Publication History
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Abstract

Military troops rely on maintenance management projects and operations to preserve the materials’ ordinary conditions or restore them to combat or military training. Maintenance management in the defense domain has its particularities, such as those related to the type of equipment operated, the environment and operating conditions, the need to maintain equipment readiness in cases of external aggression, and the security of the information. This study aims to understand the challenges, principles, scenarios, techniques, and open questions of predictive maintenance (PdM) in the military domain. We conducted a systematic literature review that resulted in the discussion of 43 articles, leading to the identification of 23 challenges and principles, 4 scenarios where predictive maintenance is crucial, besides discussing techniques used for PdM in the military domain. Our results contribute to understanding the perspective of PdM in the defense context.

REFERENCES

  1. [1] Army Brazilian. 2009. Administrative standards relating to weapons (normas administrativas relativas ao armamento (NARA)). Defense Ministry 1, 1 (2009). https://pqrmnt7.eb.mil.br/images/Producao/Legislacao/NARA%202009.pdf.Google ScholarGoogle Scholar
  2. [2] Army U.S.. 2019. Army regulation 750-1 army materiel maintenance policy. p. 225. https://www.kansastag.gov/AdvHTML_Upload/files/AR%20750-1%20Army%20Materiel%20Maintenance%20Policy.pdf.Google ScholarGoogle Scholar
  3. [3] Atamuradov Vepa, Medjaher Kamal, Dersin Pierre, Lamoureux Benjamin, and Zerhouni Noureddine. 2017. Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation. Int. J. Prognost. Health Manage. 8, 060 (2017), 131.Google ScholarGoogle Scholar
  4. [4] Babbar Ashish, Syrmos Vassilis L., Ortiz Estefan M., and Arita Michael M.. 2009. Advanced diagnostics and prognostics for engine health monitoring. In Proceedings of the IEEE Aerospace Conference. IEEE, 110.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Bajaj Naman S., Patange Abhishek D., Jegadeeshwaran R., Kulkarni Kaushal A., Ghatpande Rohan S., and Kapadnis Atharva M.. 2021. A Bayesian optimized discriminant analysis model for condition monitoring of face milling cutter using vibration datasets. J. Nondestruct. Eval., Diagnost. Prognost. Eng. Syst. 5, 2 (2021), 021002.Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Baker William, Nixon Steven, Banks Jeffrey, Reichard Karl, and Castelle Kaitlynn. 2020. Degrader analysis for diagnostic and predictive capabilities: A demonstration of progress in DoD CBM+ initiatives. Procedia Comput. Sci. 168 (2020), 257264.Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Balakrishnan N., Devasigamani Angello I., Anupama K. R., and Sharma Nitin. 2021. Aero-engine health monitoring with real flight data using whale optimization algorithm-based artificial neural network technique. Optic. Memory Neural Netw. 30, 1 (2021), 8096.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. [8] Banghart Marc. 2017. Identification of reverse engineering candidates utilizing machine learning and aircraft cannibalization data. Int. J. Aviat., Aeronaut., Aerospace 4, 4 (2017), 5.Google ScholarGoogle Scholar
  9. [9] Banks Jeffrey C., Lebold Mitchell, Reichard Karl M., Hines Jason A., and Brought Mark S.. 2014. Platform degrader analysis for the complex systems for the design and application of condition-based maintenance. In Proceedings of the IEEE Aerospace Conference. IEEE, 112.Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Bayoumi Abdel and Matthews Rhea. 2020. Condition-based maintenance to predictive maintenance: A use case on selected USARMY military aircraft. Int. J. COMADEM 23, 2 (2020), 38.Google ScholarGoogle Scholar
  11. [11] Behera Sourajit, Choubey Anurag, Kanani Chandresh S., Patel Yashwant Singh, Misra Rajiv, and Sillitti Alberto. 2019. Ensemble trees learning-based improved predictive maintenance using IIoT for turbofan engines. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 842850.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. [12] Blechertas Vytautas, Bayoumi Abdel, Goodman Nicholas, Shah Ronak, and Shin Yong-June. 2009. CBM fundamental research at the university of south carolina: A systematic approach to U.S. army rotorcraft CBM and the resulting tangible benefits. In Proceedings of AHS International Specialists’ Meeting on Condition Based Maintenance.Google ScholarGoogle Scholar
  13. [13] Boller Christian. 2002. Ageing aircraft, health monitoring and maintenance.Google ScholarGoogle Scholar
  14. [14] Byington Carl S., Roemer Michael J., and Galie Thomas. 2002. Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft]. In Proceedings of the IEEE Aerospace Conference, Vol. 6. IEEE, 66.Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Campos Frank T., Mills W. Nathaniel, and Graves Michael L.. 2002. A reference architecture for remote diagnostics and prognostics applications. In Proceedings of the IEEE AUTOTESTCON. IEEE, 842853.Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Chan Teck Kai and Chin Cheng Siong. 2019. Health stages diagnostics of underwater thruster using sound features with imbalanced dataset. Neural Comput. Appl. 31, 10 (2019), 57675782.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Chen Chong, Liu Ying, Sun Xianfang, Cairano-Gilfedder Carla Di, and Titmus Scott. 2021. An integrated deep learning-based approach for automobile maintenance prediction with GIS data. Reliabil. Eng. Syst. Safety 216 (2021), 107919.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Cipollini Francesca, Oneto Luca, Coraddu Andrea, Murphy Alan John, and Anguita Davide. 2018. Condition-based maintenance of naval propulsion systems with supervised data analysis. Ocean Eng. 149 (2018), 268278.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Cook Jonathan. 2007. Reducing military helicopter maintenance through prognostics. In Proceedings of the IEEE Aerospace Conference. IEEE, 17.Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Dalzochio Jovani, Kunst Rafael, Pignaton Edison, Binotto Alecio, Sanyal Srijnan, Favilla Jose, and Barbosa Jorge. 2020. Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Comput. Industry 123 (2020), 103298.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Lopes Sofia Moreira de Andrade, Flauzino Rogério Andrade, and Altafim Ruy Alberto Corrêa. 2021. Incipient fault diagnosis in power transformers by data-driven models with over-sampled dataset. Electr. Power Syst. Res. 201 (2021), 107519.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Desell Travis, Clachar Sophine, Higgins James, and Wild Brandon. 2014. Evolving neural network weights for time-series prediction of general aviation flight data. In Proceedings of the International Conference on Parallel Problem Solving from Nature. Springer, 771781.Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Ducoffe Mélanie, Haloui Ilyass, Gupta Jayant Sen, and Supaero Isae. 2019. Anomaly detection on time series with Wasserstein GAN applied to PHM. PHM Appl. Deep Learn. Emerg. Analyt. Int. J. Prognost. Health Manage. Rev. (Special Issue) 10, 4 (2019), 112.Google ScholarGoogle Scholar
  24. [24] Fernández-Barrero David, Fontenla-Romero Oscar, Lamas-López Francisco, Novoa-Paradela David, Sanz David, et al. 2021. SOPRENE: Assessment of the Spanish Armada’s predictive maintenance tool for naval assets. Appl. Sci. 11, 16 (2021), 7322.Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Furch Jan, Nguyen Trung Tin, and Glos Josef. 2017. Diagnostics of gear fault in four-speed gearbox using vibration signal. In Proceedings of the International Conference on Military Technologies (ICMT’17). IEEE, 8792.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Giordano Danilo, Pastor Eliana, Giobergia Flavio, Cerquitelli Tania, Baralis Elena, Mellia Marco, Neri Alessandra, and Tricarico Davide. 2021. Dissecting a data-driven prognostic pipeline: A powertrain use case. Expert Syst. Appl. 180 (2021), 115109.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Homborg A. M., Tinga T., and Mol J. M. C.. 2018. Listening to corrosion. In Proceedings of the AVT-305 Research Specialists’ Meeting on Sensing Systems for Integrated Vehicle Health Management for Military Vehicles. NATO Science & Technology Organization, 114.Google ScholarGoogle Scholar
  28. [28] Hrúz Michal, Bugaj Martin, Novák Andrej, Kandera Branislav, and Badánik Benedikt. 2021. The use of UAV with infrared camera and RFID for airframe condition monitoring. Appl. Sci. 11, 9 (2021), 3737.Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Huang Min, Liu Zhen, and Tao Yang. 2020. Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion. Simul. Model. Pract. Theory 102 (2020), 101981.Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Iannace Gino, Ciaburro Giuseppe, and Trematerra Amelia. 2019. Fault diagnosis for UAV blades using artificial neural network. Robotics 8, 3 (2019), 59.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Kála Martin, Lališ Andrej, and Vittek Peter. 2019. Optimizing calculation of maintenance revision times in maintenance repair organizations. In Proceedings of the International Conference on Military Technologies (ICMT’19). IEEE, 16.Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Khatri Hiralal, Ranney Kenneth, Tom Kwok, and Rosario Romeo del. 2008. New features for diagnosis and prognosis of systems based on empirical mode decomposition. In Proceedings of the International Conference on Prognostics and Health Management. IEEE, 127.Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Killeen Patrick, Ding Bo, Kiringa Iluju, and Yeap Tet. 2019. IoT-based predictive maintenance for fleet management. Procedia Comput. Sci. 151 (2019), 607613.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. [34] Kitchenham Barbara. 2004. Procedures for performing systematic reviews. Keele University Technical Report TR/SE-0401. Software Engineering Group, Department of Computer Science, Keele University, 126.Google ScholarGoogle Scholar
  35. [35] Kitchenham Barbara, Pretorius Rialette, Budgen David, Brereton O. Pearl, Turner Mark, Niazi Mahmood, and Linkman Stephen. 2010. Systematic literature reviews in software engineering–a tertiary study. Info. Softw. Technol. 52, 8 (2010), 792805.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. [36] Kosztyán Zsolt T., Pribojszki-Németh Anikó, and Szalkai István. 2019. Hybrid multimode resource-constrained maintenance project scheduling problem. Oper. Res. Perspect. 6 (2019), 100129.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Lall Pradeep, Lowe Ryan, and Goebel Kai. 2012. Particle swarm optimization with extended Kalman filter for prognostication of accrued damage in electronics under temperature and vibration. In Proceedings of the IEEE Conference on Prognostics and Health Management. IEEE, 113.Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Lall Pradeep, Lowe Ryan, and Goebel Kai. 2012. Prognostication of accrued damage in board assemblies under thermal and mechanical stresses. In Proceedings of the IEEE 62nd Electronic Components and Technology Conference. IEEE, 14751487.Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Le Vu T., Lim Chee Peng, Mohamed Shady, Nahavandi Saeid, Yen Leong, Gallasch Guy Edward, Baker Steven, Ludovici David, Draper Nick, Wickramanayake Vish, et al. 2017. Condition monitoring of engine lubrication oil of military vehicles: A machine learning approach. In Proceedings of the 17th Australian International Aerospace Congress (AIAC’17). Engineers Australia, Royal Aeronautical Society, 718.Google ScholarGoogle Scholar
  40. [40] Leão Bruno P., Fitzgibbon Kevin T., Puttini Lucas C., and Melo Gustavo P. B. de. 2008. Cost-benefit analysis methodology for PHM applied to legacy commercial aircraft. In Proceedings of the IEEE Aerospace Conference. IEEE, 113.Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Lee Jay, Wu Fangji, Zhao Wenyu, Ghaffari Masoud, Liao Linxia, and Siegel David. 2014. Prognostics and health management design for rotary machinery systems–Reviews, methodology and applications. Mech. Syst. Signal Process. 42, 1-2 (2014), 314334.Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Lei Yaguo, Li Naipeng, Guo Liang, Li Ningbo, Yan Tao, and Lin Jing. 2018. Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mech. Syst. Signal Process. 104 (2018), 799834.Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Li Xiong, Zhao Xiao-dong, and Pu Wei. 2020. An approach for predicting digital material consumption in electronic warfare. Def. Technol. 16, 1 (2020), 263273.Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Lijun Cao, Huibin Hu, Xinjie Shao, and Guang Tian. 2007. Research on quality evaluation and fault forecast system for complicated equipments. In Proceedings of the 8th International Conference on Electronic Measurement and Instruments. IEEE, 1190.Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Lin Lin, Luo Bin, and Zhong ShiSheng. 2018. Multi-objective decision-making model based on CBM for an aircraft fleet with reliability constraint. Int. J. Prod. Res. 56, 14 (2018), 48314848.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Liu Li, Cartes David A., and Quiroga Jabid. 2007. Modeling and simulation for condition-based maintenance: A case study in navy ship application. In Proceedings of the Summer Computer Simulation Conference. 244249.Google ScholarGoogle Scholar
  47. [47] Liu Li, Logan Kevin P., Cartes David A., and Srivastava Sanjeev K.. 2007. Fault detection, diagnostics, and prognostics: Software agent solutions. IEEE Trans. Vehic. Techn. 56, 4 (2007), 16131622.Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] McNaught K. R., Zagorecki A., and Perez A. Garcia. 2011. Knowledge elicitation for predictive maintenance modelling with Bayesian networks. In 7th IMA International Conference on Modelling in Industrial Maintenance and Reliability, Cambridge. https://www.researchgate.net/publication/228980758_Knowledge_Elicitation_for_Predictive_Maintenance_Modelling_with_Bayesian_Networks/figures?lo=1.Google ScholarGoogle Scholar
  49. [49] Nixon Steve, Weichel Ryan, Reichard Karl, and Kozlowski James. 2018. A machine learning approach to diesel engine health prognostics using engine controller data. In Proceedings of the Annual Conference of the PHM Society.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Nordal Helge and El-Thalji Idriss. 2021. Lifetime benefit analysis of intelligent maintenance: Simulation modeling approach and industrial case study. Appl. Sci. 11, 8 (2021), 3487.Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Okoh Caxton, Roy Rajkumar, Mehnen Jorn, and Redding L.. 2014. Overview of remaining useful life prediction techniques in through-life engineering services. Procedia Cirp 16 (2014), 158163.Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Pal Palash, Datta Rituparna, Segev Aviv, and Yasinsac Alec. 2019. Condition-based maintenance of turbine and compressor of a CODLAG naval propulsion system using deep neural network. In Proceedings of the 6th International Conference on Artificial Intelligence and Applications (AIAP’19).Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Peschiera Franco, Dell Robert, Royset Johannes, Haït Alain, Dupin Nicolas, and Battaïa Olga. 2020. A novel solution approach with ML-based pseudo-cuts for the flight and maintenance planning problem. OR Spectrum 43, 3 (2020), 130.Google ScholarGoogle Scholar
  54. [54] Raheja D., Llinas J., Nagi Rakesh, and Romanowski C.. 2006. Data fusion/data mining-based architecture for condition-based maintenance. Int. J. Prod. Res. 44, 14 (2006), 28692887.Google ScholarGoogle ScholarCross RefCross Ref
  55. [55] Ranasinghe Kavindu, Kapoor Rohan, Gardi Alessandro, Sabatini Roberto, Wickramanayake Vishwanath, and Ludovici David. 2020. Vehicular sensor network and data analytics for a health and usage management system. Sensors 20, 20 (2020), 5892.Google ScholarGoogle ScholarCross RefCross Ref
  56. [56] Roemer Michael J., Dzakowic Jim, Orsagh Rolf F., Byington Carl S., and Vachtsevanos George. 2005. Validation and verification of prognostic and health management technologies. In Proceedings of the IEEE Aerospace Conference. IEEE, 39413947.Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Rui Jiao, Xiaofan H. E., and Yuhai L. I.. 2018. Individual aircraft life monitoring: An engineering approach for fatigue damage evaluation. Chinese J. Aeronaut. 31, 4 (2018), 727739.Google ScholarGoogle ScholarCross RefCross Ref
  58. [58] Russell Stephen and Abdelzaher Tarek. 2018. The internet of battlefield things: The next generation of command, control, communications and intelligence (C3I) decision-making. In Proceedings of the IEEE Military Communications Conference (MILCOM’18). IEEE, 737742.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. [59] Shamayleh Abdulrahim, Awad Mahmoud, and Farhat Jumana. 2020. IoT-based predictive maintenance management of medical equipment. J. Med. Syst. 44, 4 (2020), 112.Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. [60] Shao Xin Jie, Cao Li Jun, Tian Guang, Ma Qiao, Liu Jin Hua, and Hu Hui Bin. 2012. Abrasion lives simulation and prediction of key parts for breech mechanism based on Pro/E and ADAMS. In Advanced Materials Research, Vol. 591. Trans Tech Publications, 762765.Google ScholarGoogle Scholar
  61. [61] Siegel David, Ghaffari Masoud, and Lee Jay. 2008. Failure prediction of critical components in military vehicles. In Proceedings of the Society for Machinery Failure Prevention Technology.Google ScholarGoogle Scholar
  62. [62] Tagliente Daniel A., Ludwig Andrew, and Marston Derek. 2019. Condition-based maintenance+ Data collection and offloading. In Proceedings of the IEEE AUTOTESTCON. IEEE, 15.Google ScholarGoogle ScholarCross RefCross Ref
  63. [63] Tambe Sumant, Bayoumi Abdel-Moez E., Cao Alex, McCaslin Rhea, Edwards Travis, and Center Condition-based Maintenance. 2015. An extensible CBM architecture for naval fleet maintenance using open standards. In Proceedings of the Intelligent Ship Symposium.Google ScholarGoogle Scholar
  64. [64] Tinga Tiedo. 2010. Application of physical failure models to enable usage and load-based maintenance. Reliabil. Eng. Syst. Safe. 95, 10 (2010), 10611075.Google ScholarGoogle ScholarCross RefCross Ref
  65. [65] Tinga Tiedo. 2013. Predictive maintenance of military systems based on physical failure models. Chem. Eng. 33 (2013), 295300.Google ScholarGoogle Scholar
  66. [66] Tinga Tiedo, Homborg A. M., Woldman Martijn, Heerink N. A., Smeding M., Oonincx P. J., and Wal A. J. van der. 2014. Advanced predictive maintenance concepts based on the physics of failure. In Optimal Deployment of Military Systems. Technologies for Military Missions in the Next Decade. TMC Asser Press, 291314.Google ScholarGoogle Scholar
  67. [67] Tinga Tiedo, Wubben Flip, Tiddens Wieger, Wortmann Hans, and Gaalman Gerard. 2020. Dynamic maintenance based on functional usage profiles. J. Qual. Maint. Eng. 27, 1 (2020), 2142.Google ScholarGoogle ScholarCross RefCross Ref
  68. [68] Vachtsevanos George J. and Valavanis Kimon P.. 2018. A novel approach to integrated vehicle health management. In Proceedings of the AVT-305 Research Specialists Meeting on Sensing Systems for Integrated Vehicle Health Management for Military Vehicles. NATO Science & Technology Organization.Google ScholarGoogle Scholar
  69. [69] Vališ David, Žák Libor, and Pokora Ondřej. 2015. Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data. Proc. Inst. Mech. Eng., Part O: J. Risk Reliabil. 229, 1 (2015), 3645.Google ScholarGoogle Scholar
  70. [70] Vidyasagar Kotha. 2020. Optimization of aero engine utilization through improved estimation of remaining useful life (RUL) of on condition (OC) parts. Int. J. Mech. Eng. Technol. 11, 5 (2020), 3447.Google ScholarGoogle Scholar
  71. [71] Villa Valentina, Naticchia Berardo, Bruno Giulia, Aliev Khurshid, Piantanida Paolo, and Antonelli Dario. 2021. IoT open-source architecture for the maintenance of building facilities. Appl. Sci. 11, 12 (2021), 5374.Google ScholarGoogle ScholarCross RefCross Ref
  72. [72] Wang Yu-Cheng. 2018. Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network. J. Ambient Intell. Human. Comput. (2018), 19. .Google ScholarGoogle ScholarCross RefCross Ref
  73. [73] Woldman M., Tinga Tiedo, Heide Emile Van Der, and Masen Marc Arthur. 2015. Abrasive wear-based predictive maintenance for systems operating in sandy conditions. Wear 338 (2015), 316324.Google ScholarGoogle ScholarCross RefCross Ref
  74. [74] Wang Yiwei, Gogu Christian, Binaud Nicolas, Bes Christian, and Fu Jian. 2019. A model-based prognostics method for fatigue crack growth in fuselage panels. Chinese J. Aeronaut. 32, 2 (2019), 396408.Google ScholarGoogle ScholarCross RefCross Ref

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              cover image ACM Computing Surveys
              ACM Computing Surveys  Volume 55, Issue 13s
              December 2023
              1367 pages
              ISSN:0360-0300
              EISSN:1557-7341
              DOI:10.1145/3606252
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              Publication History

              • Published: 13 July 2023
              • Online AM: 3 March 2023
              • Accepted: 21 February 2023
              • Revised: 16 January 2023
              • Received: 3 February 2022
              Published in csur Volume 55, Issue 13s

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