Zobrazeno 1 - 10
of 1 758
pro vyhledávání: '"Rotating machines"'
Autor:
Afzal Ahmed Soomro, Masdi B. Muhammad, Ainul Akmar Mokhtar, Mohamad Hanif Md Saad, Najeebullah Lashari, Muhammad Hussain, Umair Sarwar, Abdul Sattar Palli
Publikováno v:
Results in Engineering, Vol 23, Iss , Pp 102700- (2024)
Rolling bearings are essential components in a wide range of equipment, such as aeroplanes, trains, and wind turbines. Bearing failure has the potential to result in complete system failure, and it accounts for approximately 45 %–50 % of failures i
Externí odkaz:
https://doaj.org/article/68e4c5a1433242acb9aa3773beb41f1b
Publikováno v:
Data in Brief, Vol 55, Iss , Pp 110620- (2024)
Data from real systems is an important resource for research in machine diagnostics and prognostics. The demand for data has increased exponentially in recent years due to the growing interest in prognostics and the development of AI technologies for
Externí odkaz:
https://doaj.org/article/1e4a993df91846609c1c7300bf65823b
Publikováno v:
IEEE Access, Vol 12, Pp 189789-189803 (2024)
Fault data from in-service rotating machines are extremely scarce. This is usually true even when healthy data are abundant, leading to the problem of class imbalance. Numerous solutions have been proposed to cope with the problem of class imbalance;
Externí odkaz:
https://doaj.org/article/7ac95725f10f4cecbf4b15bf3d74d553
Autor:
Alfredo Contin, Andrea Piccolo
Publikováno v:
IEEE Access, Vol 12, Pp 160219-160233 (2024)
A new pass/fail criterion based on the shape analysis of phase-resolved Partial Discharges (PRPD) patterns is proposed in this study to overcome the problems generated by the use of partial discharge (PD) amplitude threshold levels. Insulation system
Externí odkaz:
https://doaj.org/article/8b5be9293e094604a74c02cd4c1da0af
Publikováno v:
IEEE Access, Vol 12, Pp 144870-144889 (2024)
Rotational machines in industries often encounter uncertainties during operation, are monitored and diagnosed through machine condition monitoring. Particularly when speed varies, Artificial Intelligence (AI) provides a great deal of support in recog
Externí odkaz:
https://doaj.org/article/7dfa7848c078419a85332f9265f2f4a0
Publikováno v:
IEEE Access, Vol 12, Pp 109109-109127 (2024)
Early fault warning for large-scale high-speed rotating machinery can effectively reduce unplanned downtime and avoid major safety accidents. Aiming at the problems of difficult screening of multi-source common sensitive features, the challenging tra
Externí odkaz:
https://doaj.org/article/bb13724c918a4f58894d3b9ac9e54e33
Publikováno v:
Journal of Engineering and Applied Science, Vol 71, Iss 1, Pp 1-19 (2024)
Abstract This study presents an enhanced envelope detection technique implemented on a field-programmable gate array (FPGA) to diagnose bearing faults in rotating machinery. Bearing faults frequently result in machinery breakdowns, incurring substant
Externí odkaz:
https://doaj.org/article/b6b3016755e445ae9c4e6022ec6912dc
Autor:
Rosario V. Giuffrida, Andreas Horat, Dominik Bortis, Tim Bierewirtz, Krishnaraj Narayanaswamy, Marcus Granegger, Johann W. Kolar
Publikováno v:
IEEE Open Journal of the Industrial Electronics Society, Vol 5, Pp 359-375 (2024)
A novel implantable total artificial heart, hereinafter referred to as the ShuttlePump, is currently under development in a research collaboration between the Medical University of Vienna, the Power Electronic Systems Laboratory of ETH Zurich and Cha
Externí odkaz:
https://doaj.org/article/60a27d899f074e0089beff1196ec26a0
Publikováno v:
IEEE Access, Vol 12, Pp 29345-29361 (2024)
Artificial Intelligence (AI) is a key component in Industry 4.0. Rotating machines are critical components in manufacturing industries. In the vast world of Industry 4.0, where an IoT network acts as a monitoring and decision-making system, predictiv
Externí odkaz:
https://doaj.org/article/2e2b09d6c6c149b2b5be518a6868f6e0
Publikováno v:
Machines, Vol 12, Iss 8, p 573 (2024)
This study presents an efficient vibration-based fault detection method for rotating machines utilising the poly-coherent composite spectrum (pCCS) and machine learning techniques. pCCS combines vibration measurements from multiple bearing locations
Externí odkaz:
https://doaj.org/article/06e4d03a0b654e26b987527018ac7a75