Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model

International Journal of Advanced Manufacturing Technology

Volumn 90, Issue 5-8, 2017, Pages 2027-2036

  Wu, Haixi ^a   Yu, Zhonghua ^a   Wang, Yan ^b  

^a ZHEJIANG UNIVERSITY   (China)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Acoustic emission; Additive manufacturing; Condition monitoring; Fused deposition modeling; Hidden semi Markov model

Indexed keywords

3D PRINTERS; ACOUSTIC EMISSIONS; CONDITION MONITORING; COST BENEFIT ANALYSIS; COST REDUCTION; DEPOSITION; LAYERED MANUFACTURING; MANUFACTURE; MARKOV PROCESSES; MONITORING; PRINCIPAL COMPONENT ANALYSIS;

ACOUSTIC EMISSION SENSORS; COMPUTATIONAL COSTS; FUSED DEPOSITION MODELING; HIDDEN SEMI-MARKOV MODELING; HIDDEN SEMI-MARKOV MODELS; MACHINE CONDITION MONITORING; MAINTENANCE STRATEGIES; MONITORING METHODS;

ACOUSTIC EMISSION TESTING;

EID: 84990985644     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-016-9548-6     Document Type: Article

References (42)

1. Al-Ghamd AM, Mba D (2006) A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech Syst Signal Process 20(7):1537–1571. doi:10.1016/j.ymssp.2004.10.013
2. Armillotta A (2006) Assessment of surface quality on textured fdm prototypes. Rapid Prototyp J 12(1):35–41. doi:10.1108/13552540610637255
3. Babel R, Koshy P, Weiss M (2013) Acoustic emission spikes at workpiece edges in grinding: origin and applications. Int J Mach Tools Manuf 64:96–101. doi:10.1016/j.ijmachtools.2012.08.004
4. Bulla J, Bulla I (2006) Stylized facts of financial time series and hidden semi-Markov models. Comput Stat Data Anal 51(4):2192–2209. doi:10.1016/j.csda.2006.07.021
5. Bulla J, Bulla I, Nenadić O (2010) hsmm—an r package for analyzing hidden semi-markov models. Comput Stat Data Anal 54(3):611–619. doi:10.1016/j.csda.2008.08.025
6. Bulla J, Lagona F, Maruotti A, Picone M (2012) A multivariate hidden Markov model for the identification of sea regimes from incomplete skewed and circular time series. J Agric Biol Environ Stat 17(4):544–567. doi:10.1007/s13253-012-0110-1
7. Chen J, Jiang YC (2011) Development of hidden semi-Markov models for diagnosis of multiphase batch operation. Chem Eng Sci 66(6):1087–1099. doi:10.1016/j.ces.2010.12.009
8. Chen X, Li B (2006) Acoustic emission method for tool condition monitoring based on wavelet analysis. Int J Adv Manuf Technol 33(9-10):968–976. doi:10.1007/s00170-006-0523-5
9. US Department of Commerce N Roadmapping workshop: Measurement science for prognostics and health management of smart manufacturing systems. http://www.nist.gov/el/isd/phm4sms-workshop.cfm
10. Conner BP, Manogharan GP, Martof AN, Rodomsky LM, Rodomsky CM, Jordan DC, Limperos JW (2014) Making sense of 3-d printing: creating a map of additive manufacturing products and services. Addit Manuf 1–4:64–76. doi:10.1016/j.addma.2014.08.005
11. Dong M, He D (2007) A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mech Syst Signal Process 21(5):2248–2266. doi:10.1016/j.ymssp.2006.10.001
12. Feng Z, Liang M, Chu F (2013) Recent advances in time–frequency analysis methods for machinery fault diagnosis: a review with application examples. Mech Syst Signal Process 38(1):165–205. doi:10.1016/j.ymssp.2013.01.017
13. Geramifard O, Xu JX, Zhou JH, Li X (2011) Continuous health condition monitoring: a single hidden semi-Markov model approach, pp 1–10. doi:10.1109/ICPHM.2011.6024333
14. Geramifard O, Xu JX, Zhou JH, Li X (2012) A physically segmented hidden Markov model approach for continuous tool condition monitoring: diagnostics and prognostics. IEEE Trans Ind Inf 8(4):964–973. doi:10.1109/TII.2012.2205583
15. Gherras N, Serris E, Fevotte G (2012) Monitoring industrial pharmaceutical crystallization processes using acoustic emission in pure and impure media. Int J Pharm 439(1–2):109–119. doi:10.1016/j.ijpharm.2012.09.048.00009
16. Guédon Y (2003) Estimating hidden semi-Markov chains from discrete sequences. J Comput Graph Stat 12 (3):604–639. doi:10.1198/1061860032030
17. Hsu CW, Lin C (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425. doi:10.1109/72.991427.05208
18. Huang Y, Leu MC, Mazumder J, Donmez A (2015) Additive manufacturing: current state, future potential, gaps and needs, and recommendations. J Manuf Sci Eng 137(1):014,001–014,001. doi:10.1115/1.4028725
19. Hung CW, Lu MC (2013) Model development for tool wear effect on AE signal generation in micromilling. Int J Adv Manuf Technol 66(9–12):1845–1858. doi:10.1007/s00170-012-4464-x
20. Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510. doi:10.1016/j.ymssp.2005.09.012
21. Jian H, Lee HR, Ahn JH (2013) Detection of bearing/rail defects for linear motion stage using acoustic emission. Int J Precis Eng Manuf 14(11):2043–2046. doi:10.1007/s12541-013-0256-y
22. Jolliffe I (2002) Principal component analysis. Wiley Online Library
23. Kaiser HF (1960) The application of electronic computers to factor analysis. Educ Psychol Meas
24. Kharrat M, Ramasso E, Placet V, Boubakar M (2016) A signal processing approach for enhanced acoustic emission data analysis in high activity systems: application to organic matrix composites. Mech Syst Signal Process 70–71:1038–1055. doi:10.1016/j.ymssp.2015.08.028
25. Kral Z, Horn W, Steck J (2013) Crack propagation analysis using acoustic emission sensors for structural health monitoring systems, vol 2013. http://www.hindawi.com/journals/tswj/2013/823603/abs/
26. 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. doi:10.1016/j.ymssp.2012.09.015
27. Lin C (2014) Early fault detection and optimal maintenance control for partially observable systems subject to vibration monitoring. Ph.D thesis. https://tspace.library.utoronto.ca/handle/1807/68266
28. Liu Q, Dong M, Lv W, Geng X, Li Y (2015) A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosis. Mech Syst Signal Process 64–65:217–232. doi:10.1016/j.ymssp.2015.03.029
29. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big data: the next frontier for innovation, competition and productivity
30. Niri ED, Farhidzadeh A, Salamone S (2013) Adaptive multisensor data fusion for acoustic emission source localization in noisy environment. Struct Health Monit 12(1):59–77. doi:10.1177/1475921712462937
31. Pei E, Campbell RI, de Beer D (2011) Entry-level rp machines: how well can they cope with geometric complexity? Assem Autom 31(2):153–160. doi:10.1108/01445151111117737
32. Peng ZK, Chu FL (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18(2):199–221. doi:10.1016/S0888-3270(03)00075-X
33. Ramasso E, Placet V, Boubakar M (2015) Unsupervised consensus clustering of acoustic emission time-series for robust damage sequence estimation in composites. IEEE Trans Instrum Meas 64(12):3297–3307. doi:10.1109/TIM.2015.2450354
34. Rao P, Liu J, Roberson D, Kong ZJ, Williams C (2015) Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors. J Manuf Sci Eng 137(6):61,007. doi:10.1115/1.4029823
35. Reutzel EW, Nassar AR (2015) A survey of sensing and control systems for machine and process monitoring of directed-energy, metal-based additive manufacturing. Rapid Prototyp J 21(2):159–167. doi:10.1108/RPJ-12-2014-0177
36. Springer DB, Tarassenko L, Clifford GD (2016) Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 63(4):822–832. doi:10.1109/TBME.2015.2475278
37. Tapia G, Elwany A (2014) A review on process monitoring and control in metal-based additive manufacturing. Journal of Manufacturing Science and Engineering 136(6):060,801–060,801. doi:10.1115/1.4028540
38. Viera AJ, Garrett JM, et al. (2005) Understanding interobserver agreement: the kappa statistic. Fam Med 37(5):360–363
39. Wu H, Wang Y, Yu Z (2016) In situ monitoring of FDM machine condition via acoustic emission. Int J Adv Manuf Technol 84(5-8):11483–1495. doi:10.1007/s00170-015-7809-4
40. Yang Z, Yu Z, Wu H, Chang D (2014) Laser-induced thermal damage detection in metallic materials via acoustic emission and ensemble empirical mode decomposition. J Mater Process Technol 214(8):1617–1626. doi:10.1016/j.jmatprotec.2014.03.009
41. Yang Z, Yu Z, Xie C, Huang Y (2014) Application of Hilbert–Huang transform to acoustic emission signal for burn feature extraction in surface grinding process. Measurement 47:14–21. doi:10.1016/j.measurement.2013.08.036
42. Zaslavsky A, Perera C, Georgakopoulos D (1301) Sensing as a service and big data. arXiv:1301.0159 [cs] (2013). ArXiv:0159