Zobrazeno 1 - 10
of 137
pro vyhledávání: '"Wentao Mao"'
Publikováno v:
IEEE Access, Vol 11, Pp 131926-131938 (2023)
Bearings in actual working environments typically operate in healthy conditions, resulting in an imbalance in the data collected data. The majority of the collected data are related to bearings in healthy conditions, with insufficient data related to
Externí odkaz:
https://doaj.org/article/97c5e80a9fd04115b29e5b6f3666e091
Publikováno v:
Lubricants, Vol 11, Iss 9, p 383 (2023)
In practical industrial scenarios, mechanical equipment frequently operates within dynamic working conditions. To address the challenge posed by the incongruent data distribution between source and target domains amidst varying operational contexts,
Externí odkaz:
https://doaj.org/article/7596ed7ac60544c08076a43e53d92e1d
Publikováno v:
Sensors, Vol 23, Iss 16, p 7182 (2023)
Demand for spare parts, which is triggered by element failure, project schedule and reliability demand, etc., is a kind of sensing data to the aftermarket service of large manufacturing enterprises. Prediction of the demand for spare parts plays a cr
Externí odkaz:
https://doaj.org/article/ac4cb702dd9748cc9cae82a209cbad01
Publikováno v:
Entropy, Vol 25, Iss 5, p 764 (2023)
The demand for complex equipment aftermarket parts is mostly sporadic, showing typical intermittent characteristics as a whole, resulting in the evolution law of a single demand series having insufficient information, which restricts the prediction e
Externí odkaz:
https://doaj.org/article/f17a1cac7ede4e8a85b625cd50fca34d
Publikováno v:
IEEE Access, Vol 9, Pp 135285-135303 (2021)
Recently, data-driven remaining useful life (RUL) prediction has become a promising tool in prognostics and health management for rolling bearings. In many actual applications, however, it is not easy to collect whole-life degradation data of bearing
Externí odkaz:
https://doaj.org/article/1e2eda25372148fcad1e8440fd8eeb56
Publikováno v:
IEEE Access, Vol 9, Pp 159684-159698 (2021)
In online scenarios, the monitoring signals are collected in the form of streaming data and would raise some requirements for early fault detection (EFD) of rolling bearings: 1) enhancing the detection accuracy of online data; 2) lowering the computa
Externí odkaz:
https://doaj.org/article/72ec87c5b0f040bb89ded97470ad752f
Publikováno v:
Entropy, Vol 25, Iss 1, p 123 (2023)
In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to
Externí odkaz:
https://doaj.org/article/b9bf6e87d5f843ab911f90572a8c7f50
Publikováno v:
IEEE Access, Vol 7, Pp 9515-9530 (2019)
Due to the real working conditions and data acquisition equipment, the collected working data of bearings are actually limited. Meanwhile, as the rolling bearing works in the normal state at most times, it is easy to raise the imbalance problem of fa
Externí odkaz:
https://doaj.org/article/1f829783f78e45d9a9a17fb581e274e5
Publikováno v:
IEEE Access, Vol 7, Pp 116078-116093 (2019)
Lévy flight Shuffle Frog Leaping Algorithm (LSFLA) is a SFLA variant and enhances the performance of SFLA largely, however, it still has some defects, such as poor convergence and low efficiency. So an improved LSFLA, namely, LSFLA based on Differe
Externí odkaz:
https://doaj.org/article/c223b78a586349af8713a203ded96ca1
Publikováno v:
Sensors, Vol 22, Iss 15, p 5681 (2022)
Aiming at the online detection problem of rolling bearings, the limited amount of target bearing data leads to insufficient model in training and feature representation. It is difficult for the online detection model to construct an accurate decision
Externí odkaz:
https://doaj.org/article/c84c6c9db1194de9a34e0142549d2c25