A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
المؤلفون المشاركون
AlShorman, Omar
Irfan, Muhammad
Saad, Nordin
Zhen, D.
Haider, Noman
Glowacz, Adam
AlShorman, Ahmad
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-20، 20ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-04
دولة النشر
مصر
عدد الصفحات
20
التخصصات الرئيسية
الملخص EN
The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments.
Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications.
However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust.
Rolling bearings are considered to be the main component of IM.
Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system.
Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM.
Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains.
Artificial intelligence (AI) techniques have proven their significance in every field of digital technology.
Industrial machines, automation, and processes are the net frontiers of AI adaptation.
There are quite developed literatures that have been approaching the issues using signals and data processing techniques.
However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods.
This study highlights the advantages and performance limitations of each method.
Finally, challenges and future trends are also highlighted.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
AlShorman, Omar& Irfan, Muhammad& Saad, Nordin& Zhen, D.& Haider, Noman& Glowacz, Adam…[et al.]. 2020. A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor. Shock and Vibration،Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1212876
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
AlShorman, Omar…[et al.]. A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor. Shock and Vibration No. 2020 (2020), pp.1-20.
https://search.emarefa.net/detail/BIM-1212876
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
AlShorman, Omar& Irfan, Muhammad& Saad, Nordin& Zhen, D.& Haider, Noman& Glowacz, Adam…[et al.]. A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1212876
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1212876
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