Maintenance and repair (MR) of medical equipment is very important, performing routine maintenance not only helps the medical device to operate stably but also helps protect the patient's life. Currently, most medical devices are manufactured using the most modern scientific techniques. Hence, the medical equipment used today are sophisticated and complicated. There for, the maintenance and repair of medical equipment is equally complicated. MR is a complex process because there are many factors that affect equipment and involve interdisciplinary knowledge. MR is a very complex decision-making process and requires experts to provide accurate decisions. Currently, there are methods of diagnosing the handpiece as follows: diagnosis is based on noise, thermal image emitted by the handpiece. These methods have the same feature that they all rely on one top-undesired event to diagnose handpiece such as temperature, sound and vibration. And that is a prerequisite to being able to apply their methods. Therefore, if the user's handpiece is facing an error that is not related to temperature, sound, or vibration, their methods cannot be applied. For that reason, in this thesis an expert system was developed for a comprehensive diagnose of the handpiece. The evaluation results of our proposed expert system were reasonable with an average percentage – 85.5%. Therefore, the results support further study of the possibility of the FTA system for fault diagnosis in a practical setting. In addition, sound analysis was also performed to investigate the noise signal emanating from three types of handpiece including normal handpiece, bearing failure handpiece and stuck bearing handpiece. The results of the sound analysis can broaden the research direction for an intelligent diagnostic system, improve the accuracy and provide more precise conclusions in handpiece diagnosis.

[1] D. Y. Kim, K. Son, and K. B. Lee, “Evaluation of high-speed handpiece cutting efficiency according to bur eccentricity: An in vitro study,” Appl. Sci., vol. 9, no. 16, 2019, doi: 10.3390/app9163395.
[2] G. Williams, “Anatomy of a Handpiece : Understanding Handpiece Maintenance and Repairs,” Online, no. November, 2013.
[3] M. Wei, J. E. Dyson, and B. W. Darvell, “Failure analysis of the ball bearings of dental air turbine handpieces,” Aust. Dent. J., vol. 58, no. 4, pp. 514–521, 2013, doi: 10.1111/adj.12112.
[4] Hunter. T, “Dental Handpiece Maintenance and Repair,” pp. 1–9, 2015.
[5] H. Li, J. Huang, and S. Ji, “Bearing fault diagnosis with a feature fusion method based on an ensemble convolutional neural network and deep neural network,” Sensors (Switzerland), vol. 19, no. 9, 2019, doi: 10.3390/s19092034.
[6] Y. Nishi, H. Fushimi, K. Shimomura, and T. Hasegawa, “Performance and Internal Flow of a Dental Air Turbine Handpiece,” Int. J. Rotating Mach., vol. 2018, 2018, doi: 10.1155/2018/1826489.
[7] M. Sezdi, “Two Different Maintenance Strategies in the Hospital Environment: Preventive Maintenance for Older Technology Devices and Predictive Maintenance for Newer High-Tech Devices,” J. Healthc. Eng., vol. 2016, 2016, doi: 10.1155/2016/7267983.
[8] N. Hamdi, R. Oweis, H. A. Zraiq, and D. A. Sammour, “An intelligent healthcare management system: A new approach in work-order prioritization for medical equipment maintenance requests,” J. Med. Syst., vol. 36, no. 2, pp. 557–567, 2012, doi: 10.1007/s10916-010-9501-4.
[9] G. Ginsburg, “Human factors engineering: A tool for medical device evaluation in hospital procurement decision-making,” J. Biomed. Inform., vol. 38, no. 3, pp. 213–219, 2005, doi: 10.1016/j.jbi.2004.11.008.
[10] M. Wei, J. E. Dyson, and B. W. Darvell, “Factors affecting dental air-turbine handpiece bearing failure,” Oper. Dent., vol. 37, no. 4, 2012, doi: 10.2341/11-087-L.
[11] Y. C. Huang and P. J. Wang, “Infrared air turbine dental handpiece rotor fault diagnosis with convolutional neural network,” Sensors Mater., vol. 32, no. 11, pp. 3545–3558, 2020, doi: 10.18494/SAM.2020.2755.
[12] H. Yi-Cheng, L. Chang-Chih, and C. Po-Chou, “Prognostic diagnosis of the health status of an air-turbine dental handpiece rotor by using sound and vibration signals,” J. Vibroengineering, vol. 18, no. 3, pp. 1514–1524, 2016, doi: 10.21595/jve.2016.16735.
[13] M. Patel, P. Virparia, and D. Patel, “Web Based Fuzzy Expert System and Its Applications - a Survey Web Based Fuzzy Expert System and Its Aplications,” no. March 2012, pp. 10–15, 2014, doi: 10.5120/105-0209.
[14] A. Syberfeldt, O. Danielsson, M. Holm, and L. Wang, “Dynamic Operator Instructions Based on Augmented Reality and Rule-based Expert Systems,” Procedia CIRP, vol. 41, pp. 346–351, 2016, doi: 10.1016/j.procir.2015.12.113.
[15] S. H. Liao et al., “Expert system methodologies and applications-a decade review from 1995 to 2004,” Int. J. Eng. Inf. Syst., vol. 1, no. 2, pp. 1–10, 2005, doi: 10.1016/j.eswa.2004.08.003.
[16] K. Durga Rao, V. Gopika, V. V. S. Sanyasi Rao, H. S. Kushwaha, A. K. Verma, and A. Srividya, “Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment,” Reliab. Eng. Syst. Saf., vol. 94, no. 4, pp. 872–883, 2009, doi: 10.1016/j.ress.2008.09.007.
[17] C. T. Lin and M. J. J. Wang, “Hybrid fault tree analysis using fuzzy sets,” Reliab. Eng. Syst. Saf., vol. 58, no. 3, pp. 205–213, 1997, doi: 10.1016/S0951-8320(97)00072-0.
[18] Y. Geum, H. Seol, S. Lee, and Y. Park, “Application of fault tree analysis to the service process: Service tree analysis approach,” J. Serv. Manag., vol. 20, no. 4, pp. 433–454, 2009, doi: 10.1108/09564230910978520.
[19] W. A. Hyman and E. Johnson, “Fault tree analysis of clinical alarms,” J. Clin. Eng., vol. 33, no. 2, pp. 85–94, 2008, doi: 10.1097/01.JCE.0000305872.86942.66.
[20] T. Assaf and J. B. Dugan, “Diagnostic expert systems from dynamic fault trees,” Proc. Annu. Reliab. Maintainab. Symp., pp. 444–450, 2004, doi: 10.1109/rams.2004.1285489.