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IoT Based Predictive Maintenance Management of Medical Equipment

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Technological advancements are the main drivers of the healthcare industry as it has a high impact on delivering the best patient care. Recent years witnessed unprecedented growth in the number of medical equipment manufactured to aid high-quality patient care at a fast pace. With this growth of medical equipment, hospitals need to adopt optimal maintenance strategies that enhance the performance of their equipment and attempt to reduce their maintenance costs and effort. In this work, a Predictive Maintenance (PdM) approach is presented to help in failure diagnosis for critical equipment with various and frequent failure mode(s). The proposed approach relies on the understanding of the physics of failure, real-time collection of the right parameters using the Internet of Things (IoT) technology, and utilization of machine learning tools to predict and classify healthy and faulty equipment status. Moreover, transforming traditional maintenance into PdM has to be supported by an economic analysis to prove the feasibility and efficiency of transformation. The applicability of the approach was demonstrated using a case study from a local hospital in the United Arab Emirates (UAE) where the Vitros-Immunoassay analyzer was selected based on maintenance events and criticality assessment as a good candidate for transforming maintenance from corrective to predictive. The dominant failure mode is metering arm belt slippage due to wear out of belt and movement of pulleys which can be predicted using vibration signals. Vibration real data is collected using wireless accelerometers and transferred to a signal analyzer located on a cloud or local computer. Features extracted and selected are analyzed using Support Vector Machine (SVM) to detect the faulty condition. In terms of economics, the proposed approach proved to provide significant diagnostic and repair cost savings that can reach up to 25% and an investment payback period of one year. The proposed approach is scalable and can be used across medical equipment in large medical centers.

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References

  1. Hamdi, N., Oweis, R., Zraiq, H. A., and Sammour, D. A., An intelligent healthcare management system: A new approach in work-order prioritization for medical equipment maintenance requests. J. Med. Syst. 36(2):557–567, 2012.

    Article  Google Scholar 

  2. Gurbeta, L., Dzemic, Z., Bego, T., Sejdic, E., and Badnjevic, A., Testing of anesthesia machines and defibrillators in healthcare institutions. J. Med. Syst. 41(9):133, 2017.

    Article  Google Scholar 

  3. Stewart, R., "Getting the most of your mobile assets " http://healthcare.flexity.ca/healthcare-it-blog/2012/6/25/getting-the-most-of-your-mobile-assets.html Accessed 15 September 2019

  4. Salah, M., Osman, H., and Hosny, O., Performance-based reliability-centered maintenance planning for hospital facilities. J. Perform. Constr. Facil. 32(1):1–7, 2017.

    Google Scholar 

  5. Mahfoud, H., El Barkany, A., and El Biyaali, A., Preventive maintenance optimization in healthcare domain: Status of research and perspective. Journal of Quality and Reliability Engineering:1–10, 2016, 2016.

  6. Sipos, R., Fradkin, D., Moerchen, F., and Wang, Z., Log-based predictive maintenance," in Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1867-1876, 2014.

  7. Tinga, T., Tiddens, W., Amoiralis, F., and Politis, M., Predictive maintenance of maritime systems: models and challenges, in 27th European Safety and Reliability Conference (ESREL 2017), pp. 1–9, 2017.

  8. Swanson, L., Linking maintenance strategies to performance. Int. J. Prod. Econ. 70(3):237–244, 2001.

    Article  Google Scholar 

  9. Scheffer, C. and Girdhar, P., Practical machinery vibration analysis and predictive maintenance. Elsevier, pp. 89–133, 2004.

  10. N. R. C. Maintenance, Guide for facilities and collateral equipment, National Aeronautics and Space Administration, pp. 31–34, 2008.

  11. Castro, L., Lefebvre, E., and Lefebvre, L. A., Adding intelligence to mobile asset management in hospitals: the true value of RFID. J. Med. Syst. 37(5):9963, 2013.

    Article  Google Scholar 

  12. M. Miler, N. N. Gabaj, L. Dukic, and A.-M. Simundic, "Key Performance Indicators to Measure Improvement After Implementation of Total Laboratory Automation Abbott Accelerator a3600," J. Med. Syst., vol. 42, no. 2, p. 28, 2018.

  13. Nurdin, M. R. F., Hadiyoso, S., and Rizal, A., A low-cost internet of things (IoT) system for multi-patient ECG's monitoring, in 2016 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), pp. 7–11, 2016.

  14. A. Kupervas. "Predictive Maintenance: What’s the Economic Value?," USENET: https://www.anodot.com/blog/predictive-maintenance/, 2019 [July 04, 2019].

  15. M. R. Future. "Predictive maintenance market research report- forecast to 2022," USENET: https://www.marketwatch.com/press-release/predictive-71 maintenance-market-2019-global-industry-analysis-by-size-share-historical-analysis-top-leaders-emerging-trends-and-regional-forecast-to-2022–2019-03-18, March 2019 [July 04, 2019].

  16. An, D., Kim, N. H., and Choi, J.-H., Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliability Engineering & System Safety 133:223–236, 2015.

    Article  Google Scholar 

  17. Li, D., Wang, W., and Ismail, F., Enhanced fuzzy-filtered neural networks for material fatigue prognosis. Appl. Soft Comput. 13(1):283–291, 2013.

    Article  Google Scholar 

  18. Ahmadzadeh, F., and Lundberg, J., Remaining useful life prediction of grinding mill liners using an artificial neural network. Miner. Eng. 53:1–8, 2013.

    Article  CAS  Google Scholar 

  19. Chakraborty, K., Mehrotra, K., Mohan, C. K., and Ranka, S., Forecasting the behavior of multivariate time series using neural networks. Neural Netw. 5(6):961–970, 1992.

    Article  Google Scholar 

  20. Silva, R. et al., Proton exchange membrane fuel cell degradation prediction based on adaptive neuro-fuzzy inference systems. Int. J. Hydrog. Energy 39(21):11128–11144, 2014.

    Article  CAS  Google Scholar 

  21. Zio, E., and Di Maio, F., A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliability Engineering & System Safety 95(1):49–57, 2010.

    Article  Google Scholar 

  22. Seeger, M., Gaussian processes for machine learning. Int. J. Neural Syst. 14(02):69–106, 2004.

    Article  Google Scholar 

  23. J. Yan, Y. Liu, S. Han, and M. Qiu" ,Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine," Renew. Sust. Energ. Rev., vol. 27, pp. 613–621, 2013.

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

    Article  Google Scholar 

  25. Coppe, A., Haftka, R. T., and Kim, N. H., Uncertainty identification of damage growth parameters using nonlinear regression. AIAA J. 49(12):2818–2821, 2011.

    Article  Google Scholar 

  26. Wang, X., and Schiavone, P., Dislocations, imperfect interfaces and interface cracks in anisotropic elasticity for quasicrystals. Mathematics and Mechanics of Complex Systems 1(1):1–17, 2013.

    Article  Google Scholar 

  27. Si, X.-S., Wang, W., Hu, C.-H., Chen, M.-Y., and Zhou, D.-H., A wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mech. Syst. Signal Process. 35(1–2):219–237, 2013.

    Article  Google Scholar 

  28. Liu, Q., Dong, M., and Peng, Y., A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods. Mech. Syst. Signal Process. 32:331–348, 2012.

    Article  Google Scholar 

  29. Konar, P., and Chattopadhyay, P., Bearing fault detection of induction motor using wavelet and support vector machines (SVMs). Appl. Soft Comput. 11(6):4203–4211, 2011.

    Article  Google Scholar 

  30. Hu, Q., He, Z., Zhang, Z., and Zi, Y., Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mech. Syst. Signal Process. 21(2):688–705, 2007.

    Article  Google Scholar 

  31. Shafri, H., and Ramle, F., A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island. Inf. Technol. J. 8(1):64–70, 2009.

    Article  Google Scholar 

  32. Strecht, P., Cruz, L., Soares, C., Mendes-Moreira, J., and Abreu, R., A comparative study of classification and regression algorithms for modelling students' academic performance, in 2015 International Educational Data Mining Society, pp. 1–4, 2015.

  33. Zhang, Z., Data mining approaches for intelligent condition-based maintenance: a framework of intelligent fault diagnosis and prognosis System (IFDPS), PhD Thesis, University of Science and Technology, Trondheim, Norway, 2014.

  34. Tan, P.-N., Steinbach, M., and Kumar, V., "Introduction to data mining, Pearson education," Inc., New Delhi, 2006.

  35. Caesarendra, W., and Tjahjowidodo, T., A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing. Machines 5(4):21, 2017.

    Article  Google Scholar 

  36. Yang, Y. and Jiang, D., "Casing vibration fault diagnosis based on variational mode decomposition, local linear embedding, and support vector machine," Shock. Vib., vol. 2017, pp. 1–15, 2017.

  37. Caesarendra, W., Kosasih, B., Tieu, K., and Moodie, C. A., An application of nonlinear feature extraction-a case study for low speed slewing bearing condition monitoring and prognosis, in 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, IEEE, pp. 1713-1718, 2013.

  38. Hozo, S. P., Djulbegovic, B., and Hozo, I., Estimating the mean and variance from the median, range, and the size of a sample. BMC Med. Res. Methodol. 5(1):13, 2005.

    Article  Google Scholar 

  39. Saravanan, N., Cholairajan, S., and Ramachandran, K., Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique. Expert Syst. Appl. 36(2):3119–3135, 2009.

    Article  Google Scholar 

  40. Von Hippel, P. T., Mean, median, and skew: Correcting a textbook rule. J. Stat. Educ. 13(2):1–14, 2005.

    Google Scholar 

  41. Yu, Y., and Junsheng, C., A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J. Sound Vib. 294(1–2):269–277, 2006.

    Article  Google Scholar 

  42. Boser, B. E., Guyon, I. M., and Vapnik, V. N., A training algorithm for optimal margin classifiers, in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pp. 144–152, 2003.

  43. Robert Nichol, P., Predictive maintenance," USENET: https://www.plantservices.com/assets/knowledge_centers/azima/assets/JustifyingCBMatYourPlant.pdf, 2009 [July 04, 2019].

  44. Rivera, A., Can predictive maintenance protect your business?, USENET: https://www.businessnewsdaily.com/10920-predictive-maintenance-business.html, June 2018 [July 04, 2019].

  45. Townsend, S., UAE inflation rate rises to 2.2% in 2017, USENET: https://www.arabianbusiness.com/uae-inflation-rate-rises-2-2-in-2017%2D%2D677530.html, May 2017 [July 04, 2019].

  46. U. government. Value Added Tax (VAT), USENET: https://government.ae/en/information-and-services/finance-and-investment/taxation/valueaddedtaxvat, June 2019 [July 04, 2019].

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Correspondence to Mahmoud Awad.

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Shamayleh, A., Awad, M. & Farhat, J. IoT Based Predictive Maintenance Management of Medical Equipment. J Med Syst 44, 72 (2020). https://doi.org/10.1007/s10916-020-1534-8

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