Open Access
Issue
Int. J. Metrol. Qual. Eng.
Volume 12, 2021
Article Number 15
Number of page(s) 9
DOI https://doi.org/10.1051/ijmqe/2021011
Published online 14 June 2021
  1. A. Diez Olivan, J. Del Ser, D. Galar, B. Sierra, Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0., Inform. Fusion 50, 92–111 (2019) [Google Scholar]
  2. L. Ciani, Future trends in IM: Diagnostics, maintenance and condition monitoring for cyber-physical systems, IEEE Instru. Meas. Mag. 22, 48–49 (2019) [Google Scholar]
  3. M. Catelani, L. Ciani, D. Galar, G. Patrizi, Risk assessment of a wind turbine: a new FMECA-Based tool with RPN threshold estimation, IEEE Access 8, 8966244, 20181–20190 (2020) [Google Scholar]
  4. A. Ragab, S. Yacout, M.S. Ouali, H. Osman, Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions, J. Intell. Manuf. 30, 255–274 (2019) [Google Scholar]
  5. A. Choudhury, Vibration monitoring of rotating electrical machines: vibration monitoring, Advanced condition monitoring and fault diagnosis of electric machines, IGI Global 163–188 (2019) [Google Scholar]
  6. S. Gao, F. Shang, C. Du, Design of multichannel and multihop low-power wide-area network for aircraft vibration monitoring, IEEE Trans. Instrum. Meas. 68, 4887–4895 (2019) [Google Scholar]
  7. S. Gao, X. Zhang, C. Du, Q. Ji, A multichannel low-power wide area network with high-accuracy synchronization ability for machine vibration monitoring, IEEE Internet Things J. 6, 5040–5047 (2019) [Google Scholar]
  8. T. Addabbo, A. Fort, E. Landi, R Moretti, M. Mugnaini, L. Parri, V. Vignoli, A wearable low-cost measurement system for estimation of human exposure to vibrations, in: Proceedings of the IEEE 5th International forum on Research and Technology for Society and Industry (RTSI) , 2019, pp. 442–446 [Google Scholar]
  9. N.I. Hossain, S. Reza, M. Ali, VibNet: application of wireless sensor network for vibration monitoring using ARM, in: Proceedings of the International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) , 2019 [Google Scholar]
  10. R.W. Supler,Uncertainty error and drift evaluation considerations involving analog to digital upgrades in nuclear power plants, in: Proceedings of the 11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT, Orlando, United States , 9–14 February, 2019, pp. 1275–1283 [Google Scholar]
  11. G. D'Emilia, A. Gaspari, F. Mazzoleni, E. Natale, A. Schiavi, Calibration of tri-axial MEMS accelerometers in the low-frequency range − part 1: comparison among methods, J. Sens. Sens. Syst. 7, 245–257 (2018) [Google Scholar]
  12. B. Xu, Design of metrology equipment running management system based on the internet of things technology, in: Proceedings of the 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) , 2017, pp. 253–259 [Google Scholar]
  13. A. Carullo, Metrological management of large-scale measuring systems, IEEE Trans. Instrum. Meas. 55, 471–476 (2006) [Google Scholar]
  14. B.D. Hall, An opportunity to enhance the value of metrological traceability in digital systems, in: Proceedings of the II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4. 0 & IoT) , 2019, pp. 16–21 [Google Scholar]
  15. S. Benedikt, T. Bruns, S. Eichstädt, Methods for dynamic calibration and augmentation of digital acceleration MEMS sensors, in: Proceedings of the 19th International Congress of Metrology (CIM2019) , 2019, EDP Sciences [Google Scholar]
  16. W.S. Cheung, Effects of the sample clock of digital-output MEMS accelerometers on vibration amplitude and phase measurements, Metrologia 57, 015008 (2020) [Google Scholar]
  17. T. Addabbo, A. Fort, E. Landi, R. Moretti, M. Mugnaini, L. Parri, V. Vignoli, A Characterization system for bearing condition monitoring sensors, a case study with a low power wireless Triaxial MEMS based sensor, in: Proceedings of the2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT , 2020, pp. 11–15 [Google Scholar]
  18. D. Bismor, Analysis and comparison of vibration signals from internal combustion engine acquired using piezoelectric and MEMS accelerometers, Vib. Phys. Syst. 30, 1–8 (2019) [Google Scholar]
  19. G. D'Emilia, E. Natale, Network of MEMS sensors for condition monitoring of industrial systems: accuracy assessment of features used for diagnosis, in: Proceedings of the 17th IMEKO TC 10 and EUROLAB Virtual Conference: “Global Trends in Testing, Diagnostics & Inspection for 2030” , 20–22 October, 2020 [Google Scholar]
  20. A. Schiavi, G. D'Emilia, A. Prato, A. Gaspari, F. Mazzoleni, E. Natale, Calibration of digital 3-axis MEMS accelerometers: a double-blind «multi-bilateral» comparison, in: Proceedings of the IEEE international Workshop on Metrology for Industry 4.0 and IoT , 2020, pp. 542–547 [Google Scholar]
  21. A. Prato, F. Mazzoleni, A. Schiavi, Traceability of digital 3-axis MEMS accelerometer: simultaneous determination of main and transverse sensitivities in the frequency domain, Metrologia 53, 035013 (2020) [Google Scholar]
  22. G. D'Emilia, A. Gaspari, E. Natale, Measurement uncertainty of contact and non-contact techniques on condition monitoring of complex industrial components, J. Phys. Conf. Ser. 1110, 012005 (2018) [Google Scholar]
  23. G. D'Emilia, A. Gaspari, E. Natale, Measurements for smart manufacturing in an Industry 4.0 scenario; a case-study on a mechatronic system, in: Proceedings of Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2018 , 2018, IEEE, pp. 107–111 [Google Scholar]
  24. A. Choudhary, T. Mian, S. Fatima, Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images, Measurement 176, 109196 (2021) [Google Scholar]
  25. M. Abubakr, M.A. Hassan, G.M. Krolczyk, N. Khanna, H. Hegab, Sensors selection for tool failure detection during machining processes: a simple accurate classification model. CIRP J. Manuf. Sci. Technol. 32, 108–119 (2021) [Google Scholar]
  26. https://it.mathworks.com/help/matlab/ref/interp1.html (accessed November 10, 2020) [Google Scholar]
  27. D.C. Montgomery, Statistical quality control , McGraw-Hill, New York, (2009) [Google Scholar]

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