Wszystkie pola: Chen, Zhenfei - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Armature MMF and electromagnetic performance analysis of dual three-phase 10-pole/24-slot permanent magnet synchronous machine
Autorzy:: Chen, Zhenfei
Xing, Ning
Ma, Hongzhong
Li, Zhixin
Li, Jiayu
Fan, Chenyang
Powiązania:: https://bibliotekanauki.pl/articles/2202550.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: dual three-phase winding
fractional-slot
concentrated winding
permanent magnet synchronous machines (SCW-PMSMs)
MMF harmonic analysis
space harmonics
magnet eddy-current loss (ECL)
SVPWM voltage supply
Opis:: Fractional-slot concentrated-winding permanent magnet synchronous machines (FSCW-PMSMs) have a good prospect of application in the drive system of electric and hybrid electric vehicles. However, the armature magnetomotive force (MMF) of FSCWPMSM contains a large number of space harmonics, which induce large magnet eddycurrent loss (ECL). To solve this problem, a dual three-phase 10-pole and 24-slot winding layout is proposed.MMFharmonic analysis shows that the 1st, 7th and 17th space-harmonic winding factors of the proposed winding can be reduced by 100%, 87% and 87% respectively, compared with a dual three-phase 10-pole and 12-slot winding. Electromagnetic performances of the proposed machine under rated sinusoidal current supply and space vector pulse-width-modulated (SVPWM) voltage supply are investigated based on 2D finite-element analysis. It is shown that the proposed machine can meet the requirement of torque and efficiency in the full speed range. Especially, magnet ECL can be reduced greatly due to the reduction of the 7th and 17th space harmonics.
Źródło:: Archives of Electrical Engineering; 2023, 72, 1; 189--210
1427-4221
2300-2506
Pojawia się w:: Archives of Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Interval Prediction of Remaining Useful Life based on Convolutional Auto-Encode and Lower Upper Bound Estimation
Autorzy:: Lyu, Yi
Zhang, Qichen
Chen, Aiguo
Wen, Zhenfei
Powiązania:: https://bibliotekanauki.pl/articles/24200839.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:: remaining useful life
lower upper bound estimation
Long Short-Term Memory
prediction interval
Opis:: Deep learning is widely used in remaining useful life (RUL) prediction because it does not require prior knowledge and has strong nonlinear fitting ability. However, most of the existing prediction methods are point prediction. In practical engineering applications, confidence interval of RUL prediction is more important for maintenance strategies. This paper proposes an interval prediction model based on Long Short-Term Memory (LSTM) and lower upper bound estimation (LUBE) for RUL prediction. First, convolutional auto-encode network is used to encode the multi-dimensional sensor data into one-dimensional features, which can well represent the main degradation trend. Then, the features are input into the prediction framework composed of LSTM and LUBE for RUL interval prediction, which effectively solves the defect that the traditional LUBE network cannot analyze the internal time dependence of time series. In the experiment section, a case study is conducted using the turbofan engine data set CMAPSS, and the advantage is validated by carrying out a comparison with other methods.
Źródło:: Eksploatacja i Niezawodność; 2023, 25, 2; art. no. 165811
1507-2711
Pojawia się w:: Eksploatacja i Niezawodność
Dostawca treści:: Biblioteka Nauki

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