Multisensor Fused Fault Diagnosis for Rotation Machinery Based on Supervised Second-Order Tensor Locality Preserving Projection and Weighted k-Nearest Neighbor Classifier under Assembled Matrix Distance Metric
المؤلفون المشاركون
Wei, Fen
Wang, Gang
Ren, Bingyin
Ge, Jianghua
Wang, Yaping
المصدر
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-11-22
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
In order to sufficiently capture the useful fault-related information available in the multiple vibration sensors used in rotation machinery, while concurrently avoiding the introduction of the limitation of dimensionality, a new fault diagnosis method for rotation machinery based on supervised second-order tensor locality preserving projection (SSTLPP) and weighted k-nearest neighbor classifier (WKNNC) with an assembled matrix distance metric (AMDM) is presented.
Second-order tensor representation of multisensor fused conditional features is employed to replace the prevailing vector description of features from a single sensor.
Then, an SSTLPP algorithm under AMDM (SSTLPP-AMDM) is presented to realize dimensional reduction of original high-dimensional feature tensor.
Compared with classical second-order tensor locality preserving projection (STLPP), the SSTLPP-AMDM algorithm not only considers both local neighbor information and class label information but also replaces the existing Frobenius distance measure with AMDM for construction of the similarity weighting matrix.
Finally, the obtained low-dimensional feature tensor is input into WKNNC with AMDM to implement the fault diagnosis of the rotation machinery.
A fault diagnosis experiment is performed for a gearbox which demonstrates that the second-order tensor formed multisensor fused fault data has good results for multisensor fusion fault diagnosis and the formulated fault diagnosis method can effectively improve diagnostic accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wei, Fen& Wang, Gang& Ren, Bingyin& Ge, Jianghua& Wang, Yaping. 2016. Multisensor Fused Fault Diagnosis for Rotation Machinery Based on Supervised Second-Order Tensor Locality Preserving Projection and Weighted k-Nearest Neighbor Classifier under Assembled Matrix Distance Metric. Shock and Vibration،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1118775
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wei, Fen…[et al.]. Multisensor Fused Fault Diagnosis for Rotation Machinery Based on Supervised Second-Order Tensor Locality Preserving Projection and Weighted k-Nearest Neighbor Classifier under Assembled Matrix Distance Metric. Shock and Vibration No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1118775
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wei, Fen& Wang, Gang& Ren, Bingyin& Ge, Jianghua& Wang, Yaping. Multisensor Fused Fault Diagnosis for Rotation Machinery Based on Supervised Second-Order Tensor Locality Preserving Projection and Weighted k-Nearest Neighbor Classifier under Assembled Matrix Distance Metric. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1118775
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1118775
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