Fault diagnosis of sensors array in oil heating reactor using deep learning techniques / (Record no. 178103)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 06145namaa22004331i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | EG-GICUC |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20260210105209.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260128s2024 ua a|||frm||| 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloguing agency | EG-GICUC |
| Language of cataloging | eng |
| Transcribing agency | EG-GICUC |
| Modifying agency | EG-GICUC |
| Description conventions | rda |
| 041 0# - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| Language code of summary or abstract | eng |
| -- | ara |
| 049 ## - Acquisition Source | |
| Acquisition Source | Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 621.38 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 621.38 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.13.08.M.Sc.2024.Ma.F |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Mai Mustafa Mahmoud, |
| Preparation | preparation. |
| 245 10 - TITLE STATEMENT | |
| Title | Fault diagnosis of sensors array in oil heating reactor using deep learning techniques / |
| Statement of responsibility, etc. | by Mai Mustafa Mahmoud ; Supervisors Prof.Dr. Hanan Ahmed Kamal, Prof.Dr. Sawsan Morkos Gharghory. |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | تشخيص أعطال مجموعة أجهزة الاستشعار في مفاعل تسخين الزيت بإستخدام تقنيات التعلم العميق |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 112 pages : |
| Other physical details | illustrations ; |
| Dimensions | 30 cm. + |
| Accompanying material | CD. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rda content |
| 337 ## - MEDIA TYPE | |
| Media type term | Unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Sc)-Cairo University, 2024. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 109-112. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | Fault Detection and diagnosis of faulty sensors readings in oil heating reactor <br/>are conducted for early failure revelation and machine components preserving before <br/>the damage. The processes of fault detection, diagnosis and correction especially <br/>in oil heating reactor sensors are among the most crucial steps for reliable and <br/>proper operation inside the reactor where they are considered as important tools <br/>that enable the controller taking the best possible action to insure finally the output <br/>quality. In this thesis, firstly fault detection of different types of sensors in oil <br/>heating reactor using different types of faults with different levels is addressed. <br/>Multiple approaches based on Neural Network (NN)s such as the classical Neural <br/>Network (NN), Bidirectional Long Short Term Memory network (BiLSTM) based <br/>on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are <br/>suggested for this purpose. The suggested networks are trained and tested on real <br/>dataset sequences taken from sensors array readings of real heating reactor in Egypt. <br/>The performance comparison of the suggested networks is evaluated using different <br/>metrics such as accuracy, precision, ... , etc . For faults detection and diagnosis <br/>by the suggested networks, different types of faults are applied to sensors array <br/>of oil heating reactor such as Bias, Stuck, Drift, precision degradation and Spike <br/>fault. The suggested networks are simulated, trained and tested in this thesis using <br/>MATLAB software 2021 and the advanced tool of "DeepNetworkDesigner”. From <br/>the simulation results, the average accuracy of CNN, BiLSTM network and Classical <br/>NN for detecting all faults types at all their different levels and to all tested sensors <br/>are 98.6%, 82.1% and 88.2% respectively. The mentioned results prove that CNN <br/>outperforms the other comparative networks with highest classification accuracy in <br/>detecting all faulty sensors with all different types of faults and at all their different <br/>levels. Drift fault was the easiest type to be detected by any one of the suggested <br/>networks where the average accuracy of classical NN, BiLSTM network and CNN in <br/>detecting all faulty sensors by this type of fault over its all levels are 98.8%, 96.6 <br/>and 100% respectively. and with average accuracy of all networks reached to 98.5On <br/>the other hand, Spike fault was the hardest type to be detected by any one of the <br/>suggested networks where the average accuracy of classical NN, BiLSTM network <br/>and CNN in detecting all faulty sensors by this type of fault over its all levels are <br/>52%, 79% and 96.6% respectively and with average accuracy of all networks reached <br/>to 76.1%. The second part of this thesis is concerning by the fault type diagnosis. <br/>After detecting which sensor are Faulty, the process of diagnosis is implemented <br/>on this faulty sensor to determine the type of fault on it. From the findings of <br/>diagnosis outputs , it is found that the accuracy of CNN for faults diagnosis to all <br/>tested sensors was directly proportional to the fault level and ranged in average from <br/>almost 73.9% at 1% fault level to 99.8% at 90% fault level. <br/>CNN average for diagnosing Precision Degradation fault at its all levels and over all <br/>tested sensors was the largest value and reached to 97.1% while was the lowest for <br/>diagnosing Bias fault and reached to 85%. The simulation results prove that CNN <br/>has the superiority in faults types diagnosis with average classification accuracy <br/>reached to 91.6% calculated to all the faulty tested sensors and at all faults levels. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Issues CD | Issues also as CD. |
| 546 ## - LANGUAGE NOTE | |
| Text Language | Text in English and abstract in Arabic & English. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Electronics and Communications Engineering |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | هندسة الإلكترونيات والاتصالات الكهربية |
| 653 #1 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Fault detection |
| -- | sensor array |
| -- | oil heater reactor |
| -- | confusion matrix |
| -- | neural network |
| -- | اكتشاف الخطأ |
| -- | مجموعة أجهزة الاستشعار |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Hanan Ahmed Kamal |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sawsan Morkos Gharghory |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2024 |
| Supervisory body | Hanan Ahmed Kamal |
| -- | Sawsan Morkos Gharghory |
| Discussion body | Shawky Zaki Eid |
| -- | Heba Ahmed Abd Elsalam |
| Universities | Cairo University |
| Faculties | Faculty of Engineering |
| Department | Department of Electronics and Communications Engineering |
| 905 ## - Cataloger and Reviser Names | |
| Cataloger Name | Shimaa |
| Reviser Names | Eman Ghareb |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| Edition | 21 |
| Suppress in OPAC | No |
| Source of classification or shelving scheme | Home library | Current library | Date acquired | Inventory number | Full call number | Barcode | Date last seen | Effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | قاعة الرسائل الجامعية - الدور الاول | 28.01.2026 | 93264 | Cai01.13.08.M.Sc.2024.Ma.F | 01010110093264000 | 28.01.2026 | 28.01.2026 | Thesis |