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    Wyświetlanie 1-4 z 4
    Tytuł:
    A simulation strategy to determine the mechanical behaviour of cork-rubber composite pads for vibration isolation
    Autorzy:
    Lopes, Helena
    Silva, Susana P.
    Machado, José
    Powiązania:
    https://bibliotekanauki.pl/articles/2057983.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    cork-rubber composites
    compression
    vibration isolation
    linear regression
    finite element analysis
    Opis:
    The present work aimed to determine the performance of new cork-rubber composites, applying a modelling-based approach. The static and dynamic behaviour under compression of new composite isolation pads was determined using mathematical techniques. Linear regression was used to estimate apparent compression modulus and dynamic stiffness coefficient of compounds samples based on the effect of fillers, cork and other ingredients. Using the results obtained by regression models, finite element analysis (FEA) was applied to determine the behaviour of the same cork-rubber material but considering samples with different dimensions. The majority of the regression models presented R2 values above 90%. Also, a good agreement was found between the results obtained by the presented approach and previous experimental tests. Based on the developed methodology, the compression behaviour of new cork-rubber compounds can be accessed, improving product development stages.
    Źródło:
    Eksploatacja i Niezawodność; 2022, 24, 1; 80--88
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Improving Diesel Engine Reliability Using an Optimal Prognostic Model to Predict Diesel Engine Emissions and Performance Using Pure Diesel and Hydrogenated Vegetable Oil
    Autorzy:
    Žvirblis, Tadas
    Hunicz, Jacek
    Matijošius, Jonas
    Rimkus, Alfredas
    Kilikevičius, Artūras
    Gęca, Michał
    Powiązania:
    https://bibliotekanauki.pl/articles/28328353.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    engine’s reliability
    statistical regression analysis
    linear regression models
    ANCOVA
    MAPE
    hydrotreated vegetable oil
    Opis:
    The reliability of internal combustion engines becomes an important aspect when traditional fuels with biofuels. Therefore, the development of prognostic models becomes very important for evaluating and predicting the replacement of traditional fuels with biofuels in internal combustion engines. The models have been made to model AVL 5402 engine emission, vibration, and sound pressure parameters using a three-stage statistical regression models. The fifteen parameters might be accurately predicted by a single statistic presented here. Both fuel type (diesel fuel and HVO) and engine parameters that can be adjusted were considered, since this analysis followed the symmetry of the methods. The data analysis process included three distinct steps and symmetric statistical regression testing was performed. The algorithm examined the effectiveness of various engine settings. Finally, the optimal fixed engine parameter and the optimal statistic were used to construct an ANCOVA model. The ANCOVA model improved the accuracy of prediction for all fifteen missing parameters.
    Źródło:
    Eksploatacja i Niezawodność; 2023, 25, 4; art. no. 174358
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of remaining useful life for lithium-ion battery with multiple health indicators
    Autorzy:
    Su, Chun
    Chen, Hongjing
    Wen, Zejun
    Powiązania:
    https://bibliotekanauki.pl/articles/1841757.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    lithium-ion (Li-ion) battery
    remaining useful life
    RUL
    health indicator
    HI
    generalized regression neural network (GRNN)
    non-linear autoregressive (NAR)
    Opis:
    Lithium-ion (Li-ion) battery has become a primary energy form for a variety of engineering equipments. To ensure the equipments’ reliability, it is crucial to accurately predict Liion battery’s remaining capacity as well as its remaining useful life (RUL). In this study, we propose a novel method for Li-ion battery’s online RUL prediction, which is based on multiple health indicators (HIs) and can be derived from the battery’s historical operation data. Firstly, four types of indirect HIs are built according to the battery’s operation current, voltage and temperature data respectively. On this basis, a generalized regression neural network (GRNN) is presented to estimate the battery’s remaining capacity, and the nonlinear autoregressive approach (NAR) is applied to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising are performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.
    Źródło:
    Eksploatacja i Niezawodność; 2021, 23, 1; 176-183
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of remaining useful life for lithium-ion battery with multiple health indicators
    Autorzy:
    Su, Chun
    Chen, Hongjing
    Wen, Zejun
    Powiązania:
    https://bibliotekanauki.pl/articles/1841833.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    lithium-ion (Li-ion) battery
    remaining useful life (RUL)
    health indicator (HI)
    generalized regression neural network (GRNN)
    non-linear autoregressive (NAR)
    Opis:
    Lithium-ion (Li-ion) battery has become a primary energy form for a variety of engineering equipments. To ensure the equipments’ reliability, it is crucial to accurately predict Liion battery’s remaining capacity as well as its remaining useful life (RUL). In this study, we propose a novel method for Li-ion battery’s online RUL prediction, which is based on multiple health indicators (HIs) and can be derived from the battery’s historical operation data. Firstly, four types of indirect HIs are built according to the battery’s operation current, voltage and temperature data respectively. On this basis, a generalized regression neural network (GRNN) is presented to estimate the battery’s remaining capacity, and the nonlinear autoregressive approach (NAR) is applied to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising are performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.
    Źródło:
    Eksploatacja i Niezawodność; 2021, 23, 1; 176-183
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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