Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process
المؤلفون المشاركون
Hou, Chia-Ding
Shao, Yuehjen E.
المصدر
العدد
المجلد 2013، العدد 2013 (31 ديسمبر/كانون الأول 2013)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2013-12-09
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Due to the recent rapid growth of advanced sensing and production technologies, the monitoring and diagnosis of multivariate process operating performance have drawn increasing interest in process industries.
The multivariate statistical process control (MSPC) chart is one of the most commonly used tools for detecting process faults.
However, an out-of-control MSPC signal only indicates that process faults have intruded the underlying process.
Identifying which of the monitored quality variables is responsible for the MSPC signal is fairly difficult.
Pinpointing the responsible variable is vital for process improvement because it effectively determines the root causes of the process faults.
Accordingly, this identification has become an important research issue concerning recent multivariate process applications.
In contrast with the traditional single classifier approach, the present study proposes hybrid modeling schemes to address problems that involve a large number of quality variables in a multivariate normal process.
The proposed scheme includes multivariate adaptive regression splines (MARS), logistic regression (LR), and artificial neural network (ANN).
By applying MARS and LR techniques, we may obtain fewer but more significant quality variables, which can serve as inputs to the ANN classifier.
The performance of our proposed approaches was evaluated by conducting a series of experiments.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Shao, Yuehjen E.& Hou, Chia-Ding. 2013. Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process. Abstract and Applied Analysis،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-468056
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Shao, Yuehjen E.& Hou, Chia-Ding. Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process. Abstract and Applied Analysis No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-468056
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Shao, Yuehjen E.& Hou, Chia-Ding. Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process. Abstract and Applied Analysis. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-468056
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-468056
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