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Article
NEURAL NETWORKS OF ENGINE FAULT DIAGNOSIS BASED ON EXHAUST GAS ANALYSIS

Authors: Rafil M. Laftah --- Qusai T. Abd-Alwahab --- qusaith@yahoo.com
Journal: University of Thi-Qar Journal for Engineering Sciences مجلة جامعة ذي قار للعلوم الهندسية ISSN: 26645564/26645572 Year: 2013 Volume: 4 Issue: 1 Pages: 58-73
Publisher: Thi-Qar University جامعة ذي قار

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Abstract

This work uses the Artificial Neural Networks (ANNs) for fault diagnosis of a single cylinder four stroke gasoline generator type (Astra Korea AST11700). One normal and fourteen faulty conditions are examined experimentally to produce a realistic data set, which is to be used for the training and validation of the ANNs. The resulted data was in the form of exhaust gases and engine speed records for each case separately under different loading conditions. After the learning process is completed, the ANN becomes able to make a diagnosis about the gasoline engine condition when new data is presented. The data presented to the ANN system include a subset of engine faults which were selected and executed experimentally for this topic. These include, faults in carburetor, air filter, spark plug, valves, piston rings, etc. The results showed that the multi layer training algorithm is sufficient enough in diagnose engine faults under different loading conditions. It was found that the correlation coefficient values are 0.999 and 1 for the testing and training data, respectively. The results obtained in this investigation showed that the ANN-based fault diagnosis system is capable of fault diagnosis with high reliability.

َستخدِم هذا العمل الشبكاتِ العصبيةِ الاصطناعية لِتشخيص أعطالِ مولدٍ كهربائي ذو محركٍ بأسطوانةٍ واحدةً ، رباعيُ الأشواط . لقد تم إختبار أربعة عشر نوعٍ مِنَ الأعطال بالإضافة إلى الحالةِ الطبيعيةِ عملياً لغرض إنتاج مجموعة من البياناتِ الشاملة والواقعية ، إستُعمِلت هذِهِ البيانات لِتدريبِ وتحقيقِ الشبكة . كانت البياناتُ الناتجةِ على شكلِ غازات العادم وسرعة المحرك المسجلة لكل حالة على حدة في ظل ظروف تحميل مختلفة . بعد انتهاء عملية التعلم، تكون الشبكة قادرة على التشخيص عند استخدام بيانات جديدة. البيانات المدخلة إلى الشبكة العصبية الصناعية تتضمّن مجموعة من أعطال المحرك التي نفذت بشكل تجريبي لهذا الغرض. ومن هذه الأعطال، خلل في نظام المكربنة، نظام ترشيح الهواء، شمعة القدح، الصمامات، وحلقات المكبس … الخ. أظهرت النتائج أن خوارزمية تدريب الرجوع العكسي هي كافية في تشخيص أعطال المحرك تحت ظروف التحميل المختلفة . وجد أن قيم معامل الارتباط هي 0.999 و 1 لبيانات الاختبار و التدريب ، على التوالي . وأظهرت النتائج في هذا التحقيق أن نظام تشخيص الأخطاء المستند على الشبكات العصبية قادر على اكتشاف الخطأ والتشخيص مع موثوقية عالية.


Article
MEASUREMENT OF LIQUID LEVEL IN PARTIALLY-FILLED PIPES USING A NOISE OF ELECTROMAGNETIC FLOWMETER

Authors: Muneer A. Ismael --- Rafil M. Laftah --- Mustafa N. Falih
Journal: Al-Qadisiyah Journal for Engineering Sciences مجلة القادسية للعلوم الهندسية ISSN: 19984456 Year: 2017 Volume: 10 Issue: 4 Pages: 550-564
Publisher: Al-Qadisiyah University جامعة القادسية

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Abstract

This paper investigates the measurement of liquid level in partially filled pipes utilizing an electrical noise signal (transformer signal) generated in electromagnetic flowmeter namely, transformer signal. The study was conducted by experiments and the collected data were analyzed statistically using Artificial Neural Network (ANN). The experimental study was achieved by building a laboratory rig containing the main parts of electromagnetic flowmeter. The main parameters which have been studied were the liquid level, magnetic field strength ( = 0.00809T, 0.03308T, 0.05301T), liquid temperature ( = 11º to 21.5º ) and liquid electrical conductivity ( = 0.11225, 3.08, 210, mS/cm). The collected data were analyzed using the back propagation neural network technique included in Matlab software 2009. The results show that the transformer signal is greatly influenced by variations of the liquid level inside partially filled electromagnetic flowmeter. The electrode position of ( = 160º) has had the strongest response to liquid level. The electrode position effect on the transformer signal is the greatest compared with that of the liquid temperature and the strength of the magnetic field. Generally, the transformer signal was found to be an increasing function with decreasing of liquid level.

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