Fault diagnosis of sensors array in oil heating reactor using deep learning techniques /
تشخيص أعطال مجموعة أجهزة الاستشعار في مفاعل تسخين الزيت بإستخدام تقنيات التعلم العميق
by Mai Mustafa Mahmoud ; Supervisors Prof.Dr. Hanan Ahmed Kamal, Prof.Dr. Sawsan Morkos Gharghory.
- 112 pages : illustrations ; 30 cm. + CD.
Thesis (M.Sc)-Cairo University, 2024.
Bibliography: pages 109-112.
Fault 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. يتم 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.
Text in English and abstract in Arabic & English.
Electronics and Communications Engineering هندسة الإلكترونيات والاتصالات الكهربية
Fault detection sensor array oil heater reactor confusion matrix neural network اكتشاف الخطأ مجموعة أجهزة الاستشعار