000 03100cam a2200349 a 4500
003 EG-GiCUC
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008 201103s2020 ua dh f m 000 0 eng d
040 _aEG-GiCUC
_beng
_cEG-GiCUC
041 0 _aeng
049 _aDeposite
097 _aPh.D
099 _aCai01.13.03.Ph.D.2020.Es.A
100 0 _aEssam Eldeen Naguib Mohammed Tawfik
245 1 0 _aAutomatic monitoring of the medical equipment performance using electrical signature analysis /
_cEssam Eldeen Naguib Mohammed Tawfik ; Supervised Ahmed H. Kandil , Ahmed M . Elbialy , Sahar Fawzi
246 1 5 _aالرصد التلقائى لأداء المعدات الطبية باستخدام تحليل التوقيعات الكهربائية
260 _aCairo :
_bEssam Eldeen Naguib Mohammed Tawfik ,
_c2020
300 _a84 P . :
_bcharts , facsmilies ;
_c30cm
502 _aThesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering
520 _aQuantitative measurement of the effective usability of the medical equipment is an important parameter in the quality and performance assessment. This work introduces a simple non-invasive technique for real time monitoring of medical equipment modes of operation based on its power consumption pattern. Mode of operation detection is a needed to specify the medical equipment reliability, availability, maintainability and usability. The Electrical Signature Analysis technique (ESA), is applied to monitor the overall electric current consumption of the electromechanical and power components inside the medical equipment. ESA is an extracted using the Root Mean Square (RMS) of the electric current measured by a meter interfaced to a PC via its USB port. The ESA of the medial equipment is a recorded, analyzed and correlated with the stored electric current consumption patterns of each specific mode of operation. The results were promising to accomplish the medical equipment monitoring application. This thesis presents an automatic fault detection system to increase reliability and efficient use of medical equipment. The system is an implemented based on an embedded circuit that uses real-time, external and non-invasive electric current sensor to apply Electrical Current Signature Analysis (ECSA). The Root Mean Square (RMS) of the collected data were calculated, saved and analyzed. The system has been a tested for two different models of medical equipment. Promising results were an obtained from testing two types of laboratory equipment. The system was able to detect the occurrence of different faults during equipment use in several modes of operation
530 _aIssued also as CD
653 4 _aAsset management
653 4 _aClinical engineering
653 4 _aMedical equipment monitoring
700 0 _aAhmed H. Kandil ,
_eSupervisor
700 0 _aAhmed M . Elbialy ,
_eSupervisor
700 0 _aSahar Fawzi ,
_eSupervisor
856 _uhttp://172.23.153.220/th.pdf
905 _aAmira
_eCataloger
905 _aNazla
_eRevisor
942 _2ddc
_cTH
999 _c78539
_d78539