PSO trained hybrid intelligent classifier using wavelet and statistical features for pipeline leak classification
المؤلفون المشاركون
Wail, A. H.
al-Dusari, Ibrahim H. M.
Akkar, Hanan Abd al-Rida
المصدر
Iraqi Journal of Computer, Communications and Control Engineering
العدد
المجلد 19، العدد 1 (31 يناير/كانون الثاني 2019)، ص ص. 1-9، 9ص.
الناشر
تاريخ النشر
2019-01-31
دولة النشر
العراق
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
One of the main problems in the oil industrial field is the leakage in transporting pipelines due to its effect on human society , environment, and money loss.
Therefore, the bottleneck for most researches in this subject is to minimize false alarm rate (FAR) for the adopted leak detection method.
Although some recent methods succeed in classifying the existence or absence of the leak as a binary classification problem.
But this paper proposed a novel leak detection technique which predicts the leak location and estimates its size within certain pre-defined ranges.
In order to simulate the environmental conditions for real-time operating oil pipeline, accurate simulator known as OLGA program creates the oil physical parameters.
Various methods for features extraction are considered such as statistical and wavelet techniques which are implemented to get the features from the fluid simulated waveforms.
These features are organized and fed to an ANN classifier trained by PSO algorithm to determine the leak class out of 10 suggested classes.
The proposed leak detection technique is used to simulate 18 kilometers belonging to Iraqi crude oil pipelines company operated in Baghdad.
The achieved results of the true positive rate (TPR) for the proposed applied method for the leak detection and classification of different leak classes in terms of their positions and magnitudes were about 97%.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Akkar, Hanan Abd al-Rida& Wail, A. H.& al-Dusari, Ibrahim H. M.. 2019. PSO trained hybrid intelligent classifier using wavelet and statistical features for pipeline leak classification. Iraqi Journal of Computer, Communications and Control Engineering،Vol. 19, no. 1, pp.1-9.
https://search.emarefa.net/detail/BIM-896200
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Akkar, Hanan Abd al-Rida…[et al.]. PSO trained hybrid intelligent classifier using wavelet and statistical features for pipeline leak classification. Iraqi Journal of Computer, Communications and Control Engineering Vol. 19, no. 1 (Jan. 2019), pp.1-9.
https://search.emarefa.net/detail/BIM-896200
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Akkar, Hanan Abd al-Rida& Wail, A. H.& al-Dusari, Ibrahim H. M.. PSO trained hybrid intelligent classifier using wavelet and statistical features for pipeline leak classification. Iraqi Journal of Computer, Communications and Control Engineering. 2019. Vol. 19, no. 1, pp.1-9.
https://search.emarefa.net/detail/BIM-896200
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 9
رقم السجل
BIM-896200
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر