A Self-Learning Sensor Fault Detection Framework for Industry Monitoring IoT
المؤلفون المشاركون
Liu, Yu
Yang, Yang
Lv, Xiaopeng
Wang, Lifeng
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2013، العدد 2013 (31 ديسمبر/كانون الأول 2013)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2013-10-21
دولة النشر
مصر
عدد الصفحات
8
التخصصات الرئيسية
الملخص EN
Many applications based on Internet of Things (IoT) technology have recently founded in industry monitoring area.
Thousands of sensors with different types work together in an industry monitoring system.
Sensors at different locations can generate streaming data, which can be analyzed in the data center.
In this paper, we propose a framework for online sensor fault detection.
We motivate our technique in the context of the problem of the data value fault detection and event detection.
We use the Statistics Sliding Windows (SSW) to contain the recent sensor data and regress each window by Gaussian distribution.
The regression result can be used to detect the data value fault.
Devices on a production line may work in different workloads and the associate sensors will have different status.
We divide the sensors into several status groups according to different part of production flow chat.
In this way, the status of a sensor is associated with others in the same group.
We fit the values in the Status Transform Window (STW) to get the slope and generate a group trend vector.
By comparing the current trend vector with history ones, we can detect a rational or irrational event.
In order to determine parameters for each status group we build a self-learning worker thread in our framework which can edit the corresponding parameter according to the user feedback.
Group-based fault detection (GbFD) algorithm is proposed in this paper.
We test the framework with a simulation dataset extracted from real data of an oil field.
Test result shows that GbFD detects 95% sensor fault successfully.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Yu& Yang, Yang& Lv, Xiaopeng& Wang, Lifeng. 2013. A Self-Learning Sensor Fault Detection Framework for Industry Monitoring IoT. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-1010444
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Yu…[et al.]. A Self-Learning Sensor Fault Detection Framework for Industry Monitoring IoT. Mathematical Problems in Engineering No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-1010444
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Yu& Yang, Yang& Lv, Xiaopeng& Wang, Lifeng. A Self-Learning Sensor Fault Detection Framework for Industry Monitoring IoT. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-1010444
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1010444
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