Skip to main content
SpringerLink
Log in

A review of prognostics and health management of machine tools

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper presents a survey of the applications of prognostics and health management maintenance strategy to machine tools. A complete perspective on this Industry 4.0 cutting-edge maintenance policy, through the analysis of all its preliminary phases, is given as an introduction. Then, attention is given to prognostics, whose different approaches are briefly classified and explained, pointing out their advantages and shortcomings. After that, all the works on prognostics of machine tools and their main subsystem are reviewed, highlighting current open research areas for improvement.

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

Similar content being viewed by others

Notes

  1. See [23] for a historical-perspective clear introduction to the different inference paradigms today existing

References

  1. Abellan-Nebot J, Romero Subirón F (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1-4):237–257

    Google Scholar 

  2. Abu-Mostafa YS, Magdon-Ismail M, Lin HT (2012) Learning from data, vol 4. AMLBook, New York

    Google Scholar 

  3. Albertelli P, Goletti M, Torta M, Salehi M, Monno M (2016) Model-based broadband estimation of cutting forces and tool vibration in milling through in-process indirect multiple-sensors measurements. Int J Adv Manuf Tech 82(5-8):779–796

    Google Scholar 

  4. Albrecht A, Park SS, Altintas Y, Pritschow G (2005) High frequency bandwidth cutting force measurement in milling using capacitance displacement sensors. Int J Mach Tools Manuf 45(9):993–1008

    Google Scholar 

  5. Ao Y, Qiao G (2010) Prognostics for drilling process with wavelet packet decomposition. Int J Adv Manuf Tech 50(1-4):47–52

    Google Scholar 

  6. Bektas O, Marshall J, Jones JA (2019) Comparison of computational prognostic methods for complex systems under dynamic regimes: a review of perspectives. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-019-09339-7

  7. Benkedjouh T, Medjaher K, Zerhouni N, Rechak S (2015) Health assessment and life prediction of cutting tools based on support vector regression. J Intell Manuf 26(2):213–223

    Google Scholar 

  8. Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, berlin

    MATH  Google Scholar 

  9. Bunks C, McCarthy D, Al-Ani T (2000) Condition-based maintenance of machines using hidden markov models. Mech Syst Signal Process 14(4):597–612

    Google Scholar 

  10. Byington CS, Watson M, Edwards D, Dunkin B (2003) In-line health monitoring system for hydraulic pumps and motors. In: Aerospace conference, 2003. proceedings. 2003 IEEE, vol. 7, pp. 3279–3287. IEEE

  11. Byington CS, Watson M, Edwards D, Stoelting P (2004) A model-based approach to prognostics and health management for flight control actuators. In: 2004 IEEE aerospace conference proceedings (IEEE Cat. No. 04TH8720), vol. 6, pp. 3551–3562. IEEE

  12. Cao H, Zhang X, Chen X (2017) The concept and progress of intelligent spindles: a review. Int J Mach Tools Manuf 112: 21–52

    Google Scholar 

  13. Catelani M, Ciani L, Cristaldi L, Faifer M, Lazzaroni M, Khalil M (2015) Toward a new definition of fmeca approach. In: 2015 IEEE International instrumentation and measurement technology conference (i2MTC) proceedings, pp. 981–986. IEEE

  14. Catelani M, Ciani L, Cristaldi L, Faifer M, Lazzaroni M, Rinaldi P (2011) Fmeca technique on photovoltaic module. In: 2011 IEEE International instrumentation and measurement technology conference, pp. 1–6. IEEE

  15. Cecati C (2015) A survey of fault diagnosis and fault-tolerant techniques—part ii: Fault diagnosis with knowledge-based and hybrid/active approaches IEEE Transactions on Industrial Electronics

  16. Chen F, Chen X, Xie Q, Xu B (2015) Reliability analysis of numerical control lathe based on the field data. In: 2015 6Th international conference on manufacturing science and engineering. Atlantis press

  17. Coble J, Wesley Hines J (2009) Identifying optimal prognostic parameters from data: a genetic algorithms approach

  18. Coppe A, Haftka RT, Kim NH, Yuan FG (2009) Reducing uncertainty in damage growth properties by structural health monitoring

  19. Cristaldi L, Khalil M, Soulatiantork P (2017) A root cause analysis and a risk evaluation of pv balance of system failures. Acta Imeko 6:113–120

    Google Scholar 

  20. Cristaldi L, Leone G, Ottoboni R, Subbiah S, Turrin S (2016) A comparative study on data-driven prognostic approaches using fleet knowledge. In: Instrumentation and measurement technology conference proceedings (I2MTC), 2016 IEEE International, pp. 1–6. IEEE

  21. Djurdjanovic D, Lee J, Ni J (2003) Watchdog agent—an infotronics-based prognostics approach for product performance degradation assessment and prediction. Adv Eng Inform 17(3-4):109–125

    Google Scholar 

  22. Drouillet C, Karandikar J, Nath C, Journeaux AC, El Mansori M, Kurfess T (2016) Tool life predictions in milling using spindle power with the neural network technique. J Manuf Process 22:161–168

    Google Scholar 

  23. Efron B, Hastie T (2016) Computer age statistical inference, vol. 5. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  24. Gao Z, Cecati C, Ding SX (2015) A survey of fault diagnosis and fault-tolerant techniques—part i: Fault diagnosis with model-based and signal-based approaches. IEEE Trans Ind Electron 62(6):3757–3767

    Google Scholar 

  25. Gittler T, Gontarz A, Weiss L, Wegener K (2019) A fundamental approach for data acquisition on machine tools as enabler for analytical industrie 4.0 applications. Procedia CIRP 79:586–591

    Google Scholar 

  26. Goebel K, Eklund N, Bonanni P (2006) Fusing competing prediction algorithms for prognostics. In: Aerospace conference, 2006 IEEE, pp. 10–pp. IEEE

  27. Gomes JPP, Leão BP, Vianna WO, Galvão RK, Yoneyama T (2012) Failure prognostics of a hydraulic pump using kalman filter. In: Annual conference of the prognostics and health management society

  28. Gomes JPP, Rodrigues LR, Leão BP, Galvão RKH, Yoneyama T (2018) Using degradation messages to predict hydraulic system failures in a commercial aircraft. IEEE Trans Autom Sci Eng 15(1):214–224

    Google Scholar 

  29. Goyal D, Pabla B (2015) Condition based maintenance of machine tools—a review. CIRP J Manuf Sci Technol 10:24–35. https://doi.org/10.1016/J.CIRPJ.2015.05.004

    Google Scholar 

  30. He Z, Wang S, Wang K, Li K (2012) Prognostic analysis based on hybrid prediction method for axial piston pump. In: Industrial informatics (INDIN), 2012 10th IEEE international conference on, pp. 688–692. IEEE

  31. Hu J, Tse PW (2013) A relevance vector machine-based approach with application to oil sand pump prognostics. Sensors 13(9):12663–12686

    Google Scholar 

  32. Hu L, Hu N, Zhang X, Gu F, Gao M (2013) Novelty detection methods for online health monitoring and post data analysis of turbopumps. J Mech Sci Technol 27(7):1933–1942. https://doi.org/10.1007/s12206-013-0508-x

    Google Scholar 

  33. Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice OT exts

  34. IEC (2018) Failure modes and effect analysis (FMEA and FMECA). Tech. Rep. IEC 60812, The International Electrotechnical Commission, 3, rue de varembė, Case postale 131, CH-1211 Genėve 20 Switzerland

  35. ISO Central Secretary (2012) Condition monitoring and diagnostics of machines – data interpretation and diagnostics techniques – Part 1: General guidelines. Standard ISO 13379-1:2012, International Organization for Standardization, Geneva, CH

  36. ISO Central Secretary (2015) Condition monitoring and diagnostics of machines – data interpretation and diagnostics techniques – Part 2: Data-driven applications. Standard ISO 13379-2:2015(E), International Organization for Standardization, Geneva, CH

  37. ISO Central Secretary (2015) Condition monitoring and diagnostics of machines – prognostics – Part 1: General guidelines. Standard ISO 13381-1:2015(E), International Organization for Standardization, Geneva, CH. https://www.iso.org/standard/62711.html

  38. ISO Central Secretary (2018) Condition monitoring and diagnostics of machines – general guidelines. Standard ISO 17359:2018, International Organization for Standardization, Geneva CH

  39. Iverson DL (2004) Inductive system health monitoring. In: Proceedings of the International Conference on Artificial Intelligence, IC-AI’04, 21-24 June 2004, Las Vegas, United States, pp. 21–24. NASA Ames Research Center; Moffett Field, CA, United States

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

    Google Scholar 

  41. Javed K, Gouriveau R, Li X, Zerhouni N (2018) Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model. J Intell Manuf 29(8):1873–1890

    Google Scholar 

  42. Javed K, Gouriveau R, Zerhouni N (2013) Novel failure prognostics approach with dynamic thresholds for machine degradation. In: IECON Proceedings (Industrial Electronics Conference). https://doi.org/10.1109/IECON.2013.6699844, pp 4402–4407

  43. Javed K, Gouriveau R, Zerhouni N, Zemouri R, Li X (2012) Robust, reliable and applicable tool wear monitoring and prognostic: approach based on an improved-extreme learning machine. In: 2012 IEEE Conference on prognostics and health management, pp. 1–9. IEEE

  44. Jeong YH, Cho DW (2002) Estimating cutting force from rotating and stationary feed motor currents on a milling machine. Int J Mach Tools Manuf 42(14):1559–1566

    Google Scholar 

  45. Jin T, Chen C, Chen L, Tian H, Zhu D, Jia X (2017) Failure mode and effects analysis of cnc machine tools based on spa. In: 2017 2Nd international conference on system reliability and safety (ICSRS), pp. 107–111. IEEE

  46. Jin X, Siegel D, Weiss BA, Gamel E, Wang W, Lee J, Ni J (2016) The present status and future growth of maintenance in us manufacturing: results from a pilot survey. Manufacturing review, 3

  47. Kalpakjian S, Schmid S (2013) Manufacturing engineering & technology pearson education

  48. Kan Y, Zhu X, Wang L, Xu B, Yang Z, Li H (2015) Comparison between bayesian method and lse in estimating mtbf of nc machine tools. In: 2015 International conference on computer science and mechanical automation (CSMA), pp. 251–257. IEEE

  49. Kim J, Chang H, Han D, Jang D, Oh S (2005) Cutting force estimation by measuring spindle displacement in milling process. CIRP annals 54(1):67–70

    Google Scholar 

  50. Kuhn M, Johnson K (2013) Applied predictive modeling, vol. 26. Springer, Berlin

    MATH  Google Scholar 

  51. Kumar S, Pecht M (2010) Modeling approaches for prognostics and health management of electronics. Int J Performability Eng 6(5):467–476

    Google Scholar 

  52. Kurada S, Bradley C (1997) A review of machine vision sensors for tool condition monitoring. Comput Ind 34(1):55–72

    Google Scholar 

  53. Leȧo BP, Yoneyama T, Rocha GC, Fitzgibbon KT (2008) Prognostics performance metrics and their relation to requirements, design, verification and cost-benefit. In: 2008 International conference on Prognostics and health management, PHM 2008. https://doi.org/10.1109/PHM.2008.4711429

  54. Lebold M, Thurston M (2001) Open standards for condition-based maintenance and prognostic systems. In: Maintenance and reliability conference (MARCON), vol. 200. May

  55. Lee J, Kao HA, Yang S (2014) Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp 16:3–8

    Google Scholar 

  56. Lee J, Ni J, Djurdjanovic D, Qiu H, Liao H (2006) Intelligent prognostics tools and e-maintenance. Computers in industry 57(6):476–489

    Google Scholar 

  57. Lee J, Wu F, Zhao W, Ghaffari M, Liao L, Siegel D (2014) Prognostics and health management design for rotary machinery systems—reviews, methodology and applications. Mech Syst Signal Pr 42(1-2):314–334

    Google Scholar 

  58. Lei Y, Li N, Guo L, Li N, Yan T, Lin J (2018) Machinery health prognostics: a systematic review from data acquisition to rul prediction. Mech Syst Signal Pr 104:799–834

    Google Scholar 

  59. Letot C, Serra R, Dossevi M, Dehombreux P (2016) Cutting tools reliability and residual life prediction from degradation indicators in turning process. Int J Adv Manuf Tech 86(1-4):495–506

    Google Scholar 

  60. Li P, Jia X, Feng J, Davari H, Qiao G, Hwang Y, Lee J (2018) Prognosability study of ball screw degradation using systematic methodology. Mech Syst Signal Pr 109:45–57

    Google Scholar 

  61. Li X, Er M, Lim B, Zhou J, Gan O, Rutkowski L (2010) Fuzzy regression modeling for tool performance prediction and degradation detection. Int J Neural Syst 20(5):405–419

    Google Scholar 

  62. Li X, Zhou J, Zeng H, San Wong Y, Hong GS (2006) An intelligent predictive engine for milling machine prognostic monitoring. In: 2006 4Th IEEE international conference on industrial informatics, pp. 1075–1080. IEEE

  63. Liao L (2014) Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Trans Ind Electron 61(5):2464–2472

    Google Scholar 

  64. Liao L, Köttig F (2014) Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Trans Reliab 63(1):191–207

    Google Scholar 

  65. Liao L, Pavel R (2012) Machine tool feed axis health monitoring using plug-and-prognose technology. In: Proc. proceedings of the 2012 conference of the society for machinery failure prevention technology, Dayton, Ohio

  66. Liao L, Pavel R (2013) Machinery time to failure prediction-case study and lesson learned for a spindle bearing application. In: Prognostics and health management (PHM), 2013 IEEE conference on, pp. 1–11. IEEE

  67. Liu D, Luo Y, Peng Y, Peng X, Pecht M (2012) Lithium-ion battery remaining useful life estimation based on nonlinear ar model combined with degradation feature. In: Annual conference of the prognostics and health management society, vol. 3, pp. 1803–1836

  68. Liu R, Yang B, Zio E, Chen X (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Signal Pr 108:33–47

    Google Scholar 

  69. Luo J, Namburu M, Pattipati K, Qiao L, Kawamoto M, Chigusa S (2003) Model-based prognostic techniques. In: Proceedings of the IEEE AUTOTESTCON, pp. 330–340

  70. McManus H, Hastings D (2005) A framework for understanding uncertainty and its mitigation and exploitation in complex systems. 15th Annual International Symposium of the International Council on Systems Engineering, INCOSE 2005(1):484–503. https://doi.org/10.1002/j.2334-5837.2005.tb00685.x

    Google Scholar 

  71. Montgomery DC, Runger GC (2010) Applied statistics and probability for engineers. Wiley, New York

    MATH  Google Scholar 

  72. Nedic N, Stojanovic V, Djordjevic V (2015) Optimal control of hydraulically driven parallel robot platform based on firefly algorithm. Nonlinear Dynamics 82(3):1457–1473. https://doi.org/10.1007/s11071-015-2252-5

    MathSciNet  MATH  Google Scholar 

  73. Nystad BH, Gola G, Hulsund JE (2012) Lifetime models for remaining useful life estimation with randomly distributed failure thresholds. In: Proceedings of first european conference of the Prognostics and health management society, pp 141–147

  74. de Oliveira Bizarria C, Yoneyama T (2009) Prognostics and health monitoring for an electro-hydraulic flight control actuator. In: 2009 IEEE Aerospace conference, pp. 1–9. IEEE

  75. Oraby S, Hayhurst D (2004) Tool life determination based on the measurement of wear and tool force ratio variation. Int J Mach Tools Manuf 44(12-13):1261–1269

    Google Scholar 

  76. Orchard M, Kacprzynski G, Goebel K, Saha B, Vachtsevanos G (2008) Advances in uncertainty representation and management for particle filtering applied to prognostics. In: 2008 International conference on Prognostics and health management, PHM 2008. https://doi.org/10.1109/PHM.2008.4711433

  77. Park SS, Altintas Y (2004) Dynamic compensation of spindle integrated force sensors with kalman filter. J Dyn Syst Measurement Contrl 126(3):443–452

    Google Scholar 

  78. Patil RB, Kothavale BS, Waghmode LY (2019) Selection of time-to-failure model for computerized numerical control turning center based on the assessment of trends in maintenance data. Proceedings of the institution of mechanical engineers, part o: journal of risk and reliability 233(2):105–117

    Google Scholar 

  79. Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Tech 50(1-4):297–313

    Google Scholar 

  80. Pršiċ D, Nediċ N, Stojanoviċ V (2017) A nature inspired optimal control of pneumatic-driven parallel robot platform. Proceedings of the institution of mechanical engineers, part c: journal of mechanical engineering science 231(1):59–71. https://doi.org/10.1177/0954406216662367

    Google Scholar 

  81. Qiu J, Seth BB, Liang SY, Zhang C (2002) Damage mechanics approach for bearing lifetime prognostics. Mech Syst Signal Processing 16(5):817–829

    Google Scholar 

  82. Rabah B, Benkedjouh T, Said R (2019) Tool wear condition monitoring based on blind source separation and wavelet transform. Lecture Notes in Electrical Engineering 522:377–389

    Google Scholar 

  83. Ramasso E, Rombaut M, Zerhouni N (2013) Joint prediction of continuous and discrete states in time-series based on belief functions. IEEE Transactions on Cybernetics 43(1):37–50. https://doi.org/10.1109/TSMCB.2012.2198882

    Google Scholar 

  84. Roemer MJ, Dzakowic J, Orsagh RF, Byington CS, Vachtsevanos G (2005) Validation and verification of prognostic and health management technologies. IEEE Aerospace Conference Proceedings 2005:3941–3947. https://doi.org/10.1109/AERO.2005.1559699

    Google Scholar 

  85. Sankararaman S, Goebel K (2015) Uncertainty in Prognostics and health management: an overview. European conference of the prognostics and health management society, pp. 1–11

  86. Sardana D, Bhatnagar R, Pavel R, Iverson J (2015) Data driven predictive analytics for a spindle. In: 2015 IEEE International conference on big data (big data), pp. 1378–1387. IEEE

  87. Saxena A, Celaya J, Balaban E, Goebel K, Saha B, Saha S, Schwabacher M (2008) Metrics for evaluating performance of prognostic techniques. In: 2008 International conference on prognostics and health management PHM 2008. https://doi.org/10.1109/PHM.2008.4711436

  88. Saxena A, Celaya J, Saha B, Saha S, Goebel K (2009) Evaluating algorithm performance metrics tailored for prognostics. https://doi.org/10.1109/AERO.2009.4839666

  89. Saxena A, Celaya J, Saha B, Saha S, Goebel K (2010) Metrics for offline evaluation of prognostic performance. Int J Prognostics Health Management 1(1):1–20

    Google Scholar 

  90. van Schrick D (1997) Remarks on terminology in the field of supervision, fault detection and diagnosis. IFAC Proceedings 30(18):959–964

    Google Scholar 

  91. Shao Y, Nezu K (2000) Prognosis of remaining bearing life using neural networks. Proceedings of the institution of mechanical engineers Part I-journal of systems and control engineering - PROC INST MECH ENG I-J SYST C 214:217–230

    Google Scholar 

  92. Shi D, Gindy NN (2007) Tool wear predictive model based on least squares support vector machines. Mech Syst Signal Process 21(4):1799–1814

    Google Scholar 

  93. Si XS, Wang W, Hu CH, Zhou DH (2011) Remaining useful life estimation–a review on the statistical data driven approaches. European journal of operational research 213(1):1–14

    MathSciNet  Google Scholar 

  94. Soylemezoglu A, Jagannathan S, Saygin C (2010) Mahalanobis taguchi system (mts) as a prognostics tool for rolling element bearing failures. Journal of Manufacturing Science and Engineering 132(5):051014

    Google Scholar 

  95. Soylemezoglu A, Jagannathan S, Saygin C (2011) Mahalanobis-taguchi system as a multi-sensor based decision making prognostics tool for centrifugal pump failures. IEEE Trans Reliab 60(4):864–878

    Google Scholar 

  96. Spendla L, Kebisek M, Tanuska P, Hrcka L (2017) Concept of predictive maintenance of production systems in accordance with industry 4.0. In: 2017 IEEE 15Th International symposium on applied machine intelligence and informatics (SAMI), pp. 000405–000410. IEEE

  97. Stavropoulos P, Papacharalampopoulos A, Vasiliadis E, Chryssolouris G (2016) Tool wear predictability estimation in milling based on multi-sensorial data. Int J Adv Manuf Tech 82(1-4):509–521

    Google Scholar 

  98. Stojanovic V, Nedic N (2016) Joint state and parameter robust estimation of stochastic nonlinear systems. International Journal of Robust and Nonlinear Control 26:3058–3074

    MathSciNet  MATH  Google Scholar 

  99. Stojanovic V, Nedic N (2016) Robust identification of OE model with constrained output using optimal input design. Journal of the Franklin Institute 353(2):576–593. https://doi.org/10.1016/J.JFRANKLIN.2015.12.007. https://www.sciencedirect.com/science/article/pii/S0016003215004470

    MathSciNet  MATH  Google Scholar 

  100. Sun B, Zeng S, Kang R, Pecht M (2010) Benefits analysis of prognostics in systems. In: 2010 Prognostics and system health management conference, pp. 1–8. IEEE

  101. 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):1293

    Google Scholar 

  102. Swearingen K, Majkowski W, Bruggeman B, Gilbertson D, Dunsdon J, Sykes B (2007) An open system architecture for condition based maintenance overview. In: Aerospace conference, 2007 IEEE, pp. 1–8. IEEE

  103. Tang L, Kacprzynski GJ, Goebel K, Vachtsevanos G (2009) Methodologies for uncertainty management in prognostics. In: IEEE Aerospace Conference Proceedings, pp. 1–12. https://doi.org/10.1109/AERO.2009.4839668

  104. Thurston MG (2001) An open standard for web-based condition-based maintenance systems. In: AUTOTESTCON Proceedings, 2001. IEEE systems readiness technology conference, pp. 401–415. IEEE

  105. Tobon-Mejia D, Medjaher K, Zerhouni N (2012) Cnc machine tools wear diagnostic and prognostic by using dynamic bayesian networks. Mech Syst Signal Process 28:167–182

    Google Scholar 

  106. Tobon-Mejia DA, Medjaher K, Zerhouni N (2010) The iso 13381-1 standard’s failure prognostics process through an example. In: Prognostics and health management conference, 2010. PHM’10., pp. 1–12. IEEE

  107. Trinh HC, Kwon YK (2020) A data-independent genetic algorithm framework for fault-type classification and remaining useful life prediction Applied Sciences (Switzerland) 10(1). https://doi.org/10.3390/app10010368

  108. Uckun S, Goebel K, Lucas PJ (2008) Standardizing research methods for prognostics. In: 2008 International Conference on Prognostics and Health Management PHM, 2008. https://doi.org/10.1109/PHM.2008.4711437

  109. Vogl GW, Weiss BA, Helu M (2019) A review of diagnostic and prognostic capabilities and best practices for manufacturing. J Intell Manuf 30(1):79–95

    Google Scholar 

  110. Wakiru JM, Pintelon L, Muchiri PN, Chemweno PK (2019) A review on lubricant condition monitoring information analysis for maintenance decision support. Mech Syst Signal Process 118:108–132

    Google Scholar 

  111. Wang C, Cheng K, Chen X, Minton T, Rakowski R (2014) Design of an instrumented smart cutting tool and its implementation and application perspectives Smart Materials and Structures 23(3). https://doi.org/10.1088/0964-1726/23/3/035019

  112. Wang C, Cheng K, Rakowski R, Soulard J (2018) An experimental investigation on ultra-precision instrumented smart aerostatic bearing spindle applied to high speed micro-drilling. J Manuf Process 31:324–335. https://doi.org/10.1016/J.JMAPRO.2017.11.022

    Google Scholar 

  113. Wang D, Peter WT (2015) Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential monte carlo method. Mech Syst Signal Process 56:213–229

    Google Scholar 

  114. Wang M, Wang J (2012) Chmm for tool condition monitoring and remaining useful life prediction. Int J Adv Manuf Technol 59(5-8):463–471

    Google Scholar 

  115. Wen J, Gao H (2018) Remaining useful life prediction of the ball screw system based on weighted mahalanobis distance and an exponential model. Journal of Vibroengineering 20(4)

  116. Wheeler KR, Kurtoglu T, Poll SD (2010) A survey of health management user objectives in aerospace systems related to diagnostic and prognostic metrics. International Journal of Prognostics and Health Management 1(1):1–19

    Google Scholar 

  117. Wu Y, Hong G, Wong W (2014) Prognosis of the probability of failure in tool condition monitoring application-a time series based approach. Int J Adv Manuf Technol 76(1-4):513– 521

    Google Scholar 

  118. Xu X (2017) Machine tool 4.0 for the new era of manufacturing. Int J Adv Manuf Tech 92(5-8):1893–1900

    Google Scholar 

  119. Yan J, Meng Y, Lu L, Li L (2017) Industrial big data in an industry 4.0 environment: challenges, schemes, and applications for predictive maintenance. IEEE Access 5:23484– 23491

    Google Scholar 

  120. Yang Z, Chen CH, Chen F, Hao Q, Xu B (2013) Reliability analysis of machining center based on the field data. Eksploatacja i Niezawodność 15(2):147–155

    Google Scholar 

  121. Yang Z, Kan Y, Chen F, Xu B, Chen C, Yang C (2015) Bayesian reliability modeling and assessment solution for nc machine tools under small-sample data. Chinese Journal of Mechanical Engineering 28 (6):1229–1239

    Google Scholar 

  122. Yang Z, Li X, Chen C, Zhao H, Yang D, Guo J, Luo W (2019) Reliability assessment of the spindle systems with a competing risk model. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233(2):226–234

    Google Scholar 

  123. Yang Z, Zhu D, Chen C, Tian H, Guo J, Li S (2018) Reliability modelling of cnc machine tools based on the improved maximum likelihood estimation method. Mathematical Problems in Engineering, 2018

  124. Yu J, Liang S, Tang D, Liu H (2017) A weighted hidden markov model approach for continuous-state tool wear monitoring and tool life prediction. Int J Adv Manuf Technol 91(1-4): 201–211

    Google Scholar 

  125. Zhang C, Yao X, Zhang J, Jin H (2016) Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations. Sensors 16(6):795

    Google Scholar 

  126. Zhang X, Gao H, Huang H (2015) Screw performance degradation assessment based on quantum genetic algorithm and dynamic fuzzy neural network. Shock and vibration, 2015

  127. Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao R (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237

    Google Scholar 

  128. Zhu J, Yoon JM, He D, Qu Y, Bechhoefer E (2013) Lubrication oil condition monitoring and remaining useful life prediction with particle filtering. International Journal of Prognostics and Health Management 4:124–138

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, via Ponzio 34/5, 20133, Milan, Italy

    Marco Baur

  2. Department of Mechanical Engineering, Politecnico di Milano, via La Masa 1, 20156, Milan, Italy

    Paolo Albertelli & Michele Monno

  3. MUSP Macchine Utensili Sistemi di Produzione, Strada della Torre della Razza, 29122, Piacenza, Italy

    Marco Baur, Paolo Albertelli & Michele Monno

Authors
  1. Marco Baur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Albertelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michele Monno
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Baur.

Additional information

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

Baur, M., Albertelli, P. & Monno, M. A review of prognostics and health management of machine tools. Int J Adv Manuf Technol 107, 2843–2863 (2020). https://doi.org/10.1007/s00170-020-05202-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05202-3

Keywords

Search

Navigation