Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Yuekai Liu"'
A Novel Evaluation Metric Based on Dispersion of Wear Distance for In Situ Tool Condition Monitoring
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 72:1-10
Publikováno v:
IEEE Transactions on Industrial Electronics. 69:13656-13664
Publikováno v:
IEEE Transactions on Industrial Electronics. 69:9483-9493
In-situ tool condition monitoring (In-situ TCM) is vital for metal removal manufacturing which realizes on-machine diagnosis in a real-time manner. The limitation of in-situ TCM based on traditional deep learning lies in several aspects: the requirem
Publikováno v:
IEEE Transactions on Industrial Informatics. 18:5199-5208
A milling cutter is one of the most important parts of machine tools. Its working status significantly influences the precision of workpieces. Due to the complex wear mechanism, the single sensor may be difficult to acquire the complete degradation i
Autor:
Liang Qi, Yuekai Liu, Sai-Weng Sin, Xinpeng Xing, Guoxing Wang, Maurits Ortmanns, Rui P. Martins
Publikováno v:
IEEE Transactions on Circuits and Systems II: Express Briefs. 69:2623-2628
Pareto-Optimal Adaptive Loss Residual Shrinkage Network for Imbalanced Fault Diagnostics of Machines
Publikováno v:
IEEE Transactions on Industrial Informatics. 18:2233-2243
In the industrial applications of mechanical fault diagnosis, machines work in normal condition at most time. In other words, most of the collected datasets are highly imbalanced. Although deep learning has been widely applied in intelligent diagnosi
Publikováno v:
IEEE Transactions on Circuits and Systems II: Express Briefs. 69:714-718
In integrated neural signal monitoring systems, pseudo resistor (PR) is widely used to form very large time constant filters, due to its large impedance within an acceptable die area. However, its linearity (the dependency of the resistance to applie
Publikováno v:
Expert Systems with Applications. 223:119891
Publikováno v:
Neural Computing and Applications. 34:6011-6026
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-10
In industrial applications of machinery fault diagnostics, transfer learning is often used to transfer the knowledge learned from the source domain including labeled data to the target domain containing unlabeled data. This method follows the assumpt