Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM
المؤلفون المشاركون
Yuan, Haodong
Chen, Jin
Dong, Guangming
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-08-16
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC) and adaptive PSO-optimized support vector machine (SVM) is proposed.
In traditional LLC, each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases.
To address these problems, an improved LLC algorithm based on adaptive and weighted bases is proposed.
Firstly, preliminary features are obtained by wavelet packet node energy.
Then, dictionary learning with class-wise K-SVD algorithm is implemented.
Subsequently, based on the learned dictionary the LLC codes can be solved using the improved LLC algorithm.
Finally, SVM optimized by adaptive particle swarm optimization (PSO) is utilized to classify the discriminative LLC codes and thus bearing fault diagnosis is realized.
In the dictionary leaning stage, other methods such as selecting the samples themselves as dictionary and K-means are also conducted for comparison.
The experiment results show that the LLC codes can effectively extract the bearing fault characteristics and the improved LLC outperforms traditional LLC.
The dictionary learned by class-wise K-SVD achieves the best performance.
Additionally, adaptive PSO-optimized SVM can greatly enhance the classification accuracy comparing with SVM using default parameters and linear SVM.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yuan, Haodong& Chen, Jin& Dong, Guangming. 2017. Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-16.
https://search.emarefa.net/detail/BIM-1191723
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yuan, Haodong…[et al.]. Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM. Mathematical Problems in Engineering No. 2017 (2017), pp.1-16.
https://search.emarefa.net/detail/BIM-1191723
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yuan, Haodong& Chen, Jin& Dong, Guangming. Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-16.
https://search.emarefa.net/detail/BIM-1191723
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1191723
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