Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Yuanqing Luo"'
Publikováno v:
IEEE Access, Vol 12, Pp 44799-44807 (2024)
In recent years, the amount of household waste has increased sharply, and there is an urgent need to use intelligent waste classification equipment to assist in completing waste classification tasks. However, existing garbage classification algorithm
Externí odkaz:
https://doaj.org/article/eb97dd55873346529a290e1e0bcd42ce
Publikováno v:
Actuators, Vol 13, Iss 10, p 401 (2024)
Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults i
Externí odkaz:
https://doaj.org/article/31d3aea4f2284490b942cab85051a471
Publikováno v:
Crystals, Vol 14, Iss 10, p 887 (2024)
Low-frequency noise absorbers often require large structural dimensions, constraining their development in practical applications. In order to improve space utilization, an acoustic metamaterial with a spatial double helix, called a spatial double he
Externí odkaz:
https://doaj.org/article/448d1ab913614f819fd6763fcd8afff8
Publikováno v:
Applied Sciences, Vol 13, Iss 23, p 12671 (2023)
To extract valuable characteristic information from the acoustic radiation signal of rolling bearings, a novel mathematical morphological network (MMNet) is proposed. First, a mathematical morphological network layer is constructed by leveraging the
Externí odkaz:
https://doaj.org/article/7d8e4d7824504e5d98a79b75ffc3f8d8
Publikováno v:
Sensors, Vol 23, Iss 21, p 8703 (2023)
The method of acoustic radiation signal detection not only enables contactless measurement but also provides comprehensive state information during equipment operation. This paper proposes an enhanced feature extraction network (EFEN) for fault diagn
Externí odkaz:
https://doaj.org/article/fbcaf3e314ab4d339b9b495d7224fb29
Publikováno v:
IEEE Access, Vol 8, Pp 156774-156791 (2020)
The early fault impulses of rolling bearing are often submerged by harmonic interferences and background noise. In this paper, a fault diagnosis scheme called probabilistic principal component analysis assisted optimal scale average of erosion and di
Externí odkaz:
https://doaj.org/article/a942d6a832224b93910fdd23c912bd36
Publikováno v:
IEEE Access, Vol 8, Pp 163715-163729 (2020)
As an important component of rotating machinery, the fault information of rolling element bearing is difficult to be recognized due to the background noise and harmonic frequency contained in the tested vibration signal. In order to accurately and co
Externí odkaz:
https://doaj.org/article/3e162ab68efd45c9b9b47b6165a70ab2
Publikováno v:
Shock and Vibration, Vol 2020 (2020)
Early fault diagnosis of rolling element bearing is still a difficult problem. Firstly, in order to effectively extract the fault impulse signal of the bearing, a new enhanced morphological difference operator (EMDO) is constructed by combining two o
Externí odkaz:
https://doaj.org/article/6e125095f0204d039fe74a1f8a05b894
Publikováno v:
Shock and Vibration, Vol 2019 (2019)
The extraction of the vibration impulse signal plays a crucial role in the fault diagnosis of rolling element bearing. However, the detection of weak fault signals generally suffers the strong background noise. To solve this problem, a new adaptive m
Externí odkaz:
https://doaj.org/article/819c29b2026c4856ae4e362cdc69ee50
Publikováno v:
Shock and Vibration, Vol 2019 (2019)
A theoretical research on eliminating the instability vibration and improving the stability of the rotor/seal system using the inerter-based dynamic vibration absorber (IDVA) is presented in this paper. The modified Jeffcott rotor and Muszynska nonli
Externí odkaz:
https://doaj.org/article/4e7ab10d76924d909ae71cf091849733