Fault Diagnosis with Evolving Fuzzy Classifier Based on Clustering Algorithm and Drift Detection
المؤلفون المشاركون
Inacio, Maurilio
Lemos, Andre
Caminhas, Walmir
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-01-14
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
The emergence of complex machinery and equipment in several areas demands efficient fault diagnosis methods.
Several fault diagnosis methods based on different theories and approaches have been proposed in the literature.
According to the conceptof intelligent maintenance, the application of intelligent systems to accomplishfault diagnosis from process historical data has been shown to be a promising approach.
In problems involving complex nonstationary dynamic systems, an adaptive fault diagnosis system is required to cope with changes in the monitored process.
In order to address fault diagnosis in this scenario, use of the so-called “evolving intelligent systems” is suggested.
This paper proposes the application of an evolving fuzzy classifier for fault diagnosis based on a new approach that combines a recursive clustering algorithm and a drift detection method.
In this approach, the clustering updatedepends not only on a similarity measure, but also on the monitoring changes in theinput data flow.
A merging cluster mechanism was incorporated into the algorithmto enable the removal of redundant clusters.
Multivariate Gaussian membershipsfunctions are employed in the fuzzy rules to avoid information loss if there is interactionbetween variables.
The novel approach provides greater robustness to outliersand noise present in data from process sensors.
The classifier is evaluated in faultdiagnosis of a DC drive system.
In the experiments, a DC drive system fault simulatorwas used to simulate normal operation and several faulty conditions.
Outliersand noise were added to the simulated data to evaluate the robustness of the faultdiagnosis model.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Inacio, Maurilio& Lemos, Andre& Caminhas, Walmir. 2015. Fault Diagnosis with Evolving Fuzzy Classifier Based on Clustering Algorithm and Drift Detection. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1073620
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Inacio, Maurilio…[et al.]. Fault Diagnosis with Evolving Fuzzy Classifier Based on Clustering Algorithm and Drift Detection. Mathematical Problems in Engineering No. 2015 (2015), pp.1-14.
https://search.emarefa.net/detail/BIM-1073620
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Inacio, Maurilio& Lemos, Andre& Caminhas, Walmir. Fault Diagnosis with Evolving Fuzzy Classifier Based on Clustering Algorithm and Drift Detection. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1073620
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1073620
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