000			06145namaa22004331i 4500
003			EG-GICUC
005			20260210105209.0
008			260128s2024 ua a|||frm||| 000 0 eng d
040			_aEG-GICUC _beng _cEG-GICUC _dEG-GICUC _erda
041	0		_aeng _beng _bara
049			_aDeposit
082	0	4	_a621.38
092			_a621.38 _221
097			_aM.Sc
099			_aCai01.13.08.M.Sc.2024.Ma.F
100	0		_aMai Mustafa Mahmoud, _epreparation.
245	1	0	_aFault diagnosis of sensors array in oil heating reactor using deep learning techniques / _cby Mai Mustafa Mahmoud ; Supervisors Prof.Dr. Hanan Ahmed Kamal, Prof.Dr. Sawsan Morkos Gharghory.
246	1	5	_aتشخيص أعطال مجموعة أجهزة الاستشعار في مفاعل تسخين الزيت بإستخدام تقنيات التعلم العميق
264		0	_c2024.
300			_a112 pages : _billustrations ; _c30 cm. + _eCD.
336			_atext _2rda content
337			_aUnmediated _2rdamedia
338			_avolume _2rdacarrier
502			_aThesis (M.Sc)-Cairo University, 2024.
504			_aBibliography: pages 109-112.
520		3	_aFault Detection and diagnosis of faulty sensors readings in oil heating reactor are conducted for early failure revelation and machine components preserving before the damage. The processes of fault detection, diagnosis and correction especially in oil heating reactor sensors are among the most crucial steps for reliable and proper operation inside the reactor where they are considered as important tools that enable the controller taking the best possible action to insure finally the output quality. In this thesis, firstly fault detection of different types of sensors in oil heating reactor using different types of faults with different levels is addressed. Multiple approaches based on Neural Network (NN)s such as the classical Neural Network (NN), Bidirectional Long Short Term Memory network (BiLSTM) based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are suggested for this purpose. The suggested networks are trained and tested on real dataset sequences taken from sensors array readings of real heating reactor in Egypt. The performance comparison of the suggested networks is evaluated using different metrics such as accuracy, precision, ... , etc . For faults detection and diagnosis by the suggested networks, different types of faults are applied to sensors array of oil heating reactor such as Bias, Stuck, Drift, precision degradation and Spike fault. The suggested networks are simulated, trained and tested in this thesis using MATLAB software 2021 and the advanced tool of "DeepNetworkDesigner”. From the simulation results, the average accuracy of CNN, BiLSTM network and Classical NN for detecting all faults types at all their different levels and to all tested sensors are 98.6%, 82.1% and 88.2% respectively. The mentioned results prove that CNN outperforms the other comparative networks with highest classification accuracy in detecting all faulty sensors with all different types of faults and at all their different levels. Drift fault was the easiest type to be detected by any one of the suggested networks where the average accuracy of classical NN, BiLSTM network and CNN in detecting all faulty sensors by this type of fault over its all levels are 98.8%, 96.6 and 100% respectively. and with average accuracy of all networks reached to 98.5On the other hand, Spike fault was the hardest type to be detected by any one of the suggested networks where the average accuracy of classical NN, BiLSTM network and CNN in detecting all faulty sensors by this type of fault over its all levels are 52%, 79% and 96.6% respectively and with average accuracy of all networks reached to 76.1%. The second part of this thesis is concerning by the fault type diagnosis. After detecting which sensor are Faulty, the process of diagnosis is implemented on this faulty sensor to determine the type of fault on it. From the findings of diagnosis outputs , it is found that the accuracy of CNN for faults diagnosis to all tested sensors was directly proportional to the fault level and ranged in average from almost 73.9% at 1% fault level to 99.8% at 90% fault level. CNN average for diagnosing Precision Degradation fault at its all levels and over all tested sensors was the largest value and reached to 97.1% while was the lowest for diagnosing Bias fault and reached to 85%. The simulation results prove that CNN has the superiority in faults types diagnosis with average classification accuracy reached to 91.6% calculated to all the faulty tested sensors and at all faults levels.
520		3	_aيتم اكتشاف القراءات الخاطئة لحساسات الحرارة المعطوبة بداخل مفاعل التسخين للزيت الخام بإستخدام تقنيات الذكاء الإصطناعي كتقنية الشبكة العصبية ذات الطبقات المتصلة كليا وتقنية الشبكة العصبية المتكررة ثنائية الإتجاة ذات الذاكرة القصيرة والطويلة المدي وإيضا الشبكة العصبية الإلتفافية.
530			_aIssues also as CD.
546			_aText in English and abstract in Arabic & English.
650		0	_aElectronics and Communications Engineering
650		0	_aهندسة الإلكترونيات والاتصالات الكهربية
653		1	_aFault detection _asensor array _aoil heater reactor _aconfusion matrix _aneural network _aاكتشاف الخطأ _aمجموعة أجهزة الاستشعار
700	0		_aHanan Ahmed Kamal _ethesis advisor.
700	0		_aSawsan Morkos Gharghory _ethesis advisor.
900			_b01-01-2024 _cHanan Ahmed Kamal _cSawsan Morkos Gharghory _dShawky Zaki Eid _dHeba Ahmed Abd Elsalam _UCairo University _FFaculty of Engineering _DDepartment of Electronics and Communications Engineering
905			_aShimaa _eEman Ghareb
942			_2ddc _cTH _e21 _n0
999			_c178103