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Wyświetlanie 1-2 z 2
Tytuł:
Semi-Markov model of multi-modal transport operation
Autorzy:
Grabski, F.
Powiązania:
https://bibliotekanauki.pl/articles/242572.pdf
Data publikacji:
2017
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
semi-Markov model
multistage transport operation
reliability function
mean time to failure
Opis:
Multi-modal transport means the transport of the objects through at least two different carriers of any combination of simple tasks of transport carriers (by truck, by train, by ship or by plane). A Semi-Markov (SM) model of multi-modal transport operation is presented in the article. The SM process is defined by the renewal kernel of that one. In our model, time to failure of the operation is represented by a random variable that denotes the first passage time from the given state to the subset of states. The duration of one operation cycle is a random variable representing the return time to the initial state. The appropriate theorems of the Semi-Markov processes theory allow us to calculate characteristics and parameters of the transport operation model. The article presents the example of the transport operation final part of container with cargo from Warsaw to Stockholm, where from Warsaw to Gdynia, the container is transported by lorry, from Gdynia to Karlscorona by ferry and from Karlscorona to Stockholm by truck.
Źródło:
Journal of KONES; 2017, 24, 4; 47-54
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic fuzzy approach to estimate operation time of transport device
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/247484.pdf
Data publikacji:
2011
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
failure prediction
fuzzy genetic system
material handling system
Opis:
The classic approach to evaluate the probability that an operational system is capable to operate satisfactorily and successfully perform the formulated tasks is based on availability that is coefficient which is determined based on the history of down-time and up-time occurring, while the risk-degree of down-time occurring strongly depends on the actual operational state of a system. The intelligence computational methods enable to create the diagnosis tools that allow to formulate the prognosis of operating time of a system and predict of failure occurring based on the past and actual information about system's operational state, especially genetic fuzzy systems (GFSs) that combine fuzzy approximate reasoning and capability to learn and adaptation. The paper presents the fuzzy rule-based inference system used to predict the operating time of exploitation system according to the specified operational conditions. The proposed algorithm was used to design the fuzzy model applied to estimate the operating time of a system between the actual time and predicted time of the next failure occurring under the stated operational parameters. The fuzzy system allows to prognoses the time of the predicted failure based on the operational parameters which are used to evaluate the actual operational state of the system. The attention in the paper is focused on the evolutionary computational techniques applied to design the fuzzy inference system. The paper proposes the genetic algorithm based on the Pittsburgh method and real-valued chromosomes used to optimize the knowledge base and parameters of antecedents and conclusions of the Takagi-Sugeno-Kang (TSK) fuzzy implications. The paper is the contribution to the GFSs, which aim is to find an appropriate balance between accuracy and interpretability, and also contribution to the research field on the diagnosis methods based on soft computing techniques. The evolutionary algorithm was tested for designing the fuzzy operating time predictor of material handling device.
Źródło:
Journal of KONES; 2011, 18, 4; 601-608
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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