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Update: 2021-07-01
Task „Implementation of procedures ensuring the originality of scientific papers published in the quarterly „Eksploatacja i Niezawodność – Maintenance and Reliability” financed under contract 532/P-DUN/2018 from the funds of the Minister of Science and Higher Education for science dissemination activities.
Hongyan Dui
Uogólniona miara zintegrowanej ważności komponentów jako narzędzie oceny wydajności systemu: zastosowanie w odniesieniu do układu śmigłowca
Miara zintegrowanej ważności (IIM) pozwala oceniać szybkość zmian wydajności systemu powstałych w wyniku przejścia elementu systemu z jednego stanu do drugiego. IIM pozwala rozważać scenariusze, w których szybkość przejścia elementu z jednego stanu do drugiego jest stała. Jest to jednak sprzeczne z założeniem degradacji, zgodnie z którym wydajność systemu obniża się, w związku z czym, szybkość przejścia może z upływem czasu ulegać zwiększeniu. Rozkład Weibulla opisuje żywotność danego elementu, co wykorzystuje się w wielu różnych zastosowaniach technicznych do modelowania złożonych zbiorów danych. W przedstawionej pracy, rozszerzono IIM uzyskując nową miarę ważności, która pozwala rozważać scenariusze, w których szybkość przejścia elementu z jednego stanu do drugiego w wyniku degradacji jest zależną od czasu funkcją rozkładu Weibulla. Przyjęto, że warunkowy rozkład prawdopodobieństwa elementu przebywającego w pewnym stanie jest rozkładem Weibulla, gdzie dany jest kolejny stan do którego ma przejść dany element. Badania nad nową miarą ważności umożliwiają identyfikację najważniejszych elementów podczas trzech różnych okresów czasu życia systemu, co odpowiada charakterystyce rozkładów Weibulla. Dla ilustracji, wyprowadzono pewne właściwości probabilistyczne i zastosowano rozszerzoną miarę ważności do analizy przykładu rzeczywistego układu śmigłowca.
Generalized integrated importance measure for system performance evaluation: application to a propeller plane system
The integrated importance measure (IIM) evaluates the rate of system performance change due to a component changing from one state to another. The IIM simply considers the scenarios where the transition rate of a component from one state to another is constant. This may contradict the assumption of the degradation, based on which system performance is degrading and therefore the transition rate may be increasing over time. The Weibull distribution describes the life of a component, which has been used in many different engineering applications to model complex data sets. This paper extends the IIM to a new importance measure that considers the scenarios where the transition rate of a component degrading from one state to another is a time-dependent function under the Weibull distribution. It considers the conditional probability distribution of a component sojourning at a state is the Weibull distribution, given the next state that component will jump to. The research on the new importance measure can identify the most important component during three different time periods of the system lifetime, which is corresponding to the characteristics of Weibull distributions. For illustration, the paper then derives some probabilistic properties and applies the extended importance measure to a real-world example (i.e., a propeller plane system).
Preventive maintenance of multiple components for hydraulic tension systems
Automatically controlled hydraulic tension systems adjust the tension force of a conveyor belt under different working conditions. Failures of an automatically controlled hydraulic tension system influence the performance of conveyor belts. At present, the maintenance of automatically controlled hydraulic tension systems mainly considers the replacement of components when failures occur. Considering the maintenance cost and downtime, it is impossible to repair all the failed components to improve the hydraulic tension system. One of the key problems is selecting the most valuable components for preventive maintenance. In this paper, preventive maintenance for multiple components in a hydraulic tension system is analyzed. An index is proposed to select more reliable preventive maintenance components to replace the original ones. A case study is given to demonstrate the proposed method. When the cost budget increases, there are three different variations in the number of components for selective preventive maintenance (SPM).
Opportunistic maintenance strategy of a Heave Compensation System for expected performance degradation
In the marine industry, heave compensation systems are applied to marine equipment to compensate for the adverse effects of waves and the hydraulic system is usually used as the power system of heave compensation systems. This article introduces importance theory to the opportunistic maintenance (OM) strategy to provide guidance for the maintenance of heave compensation systems. The working principle of a semi-active heave compensation system and the specific working states of its hydraulic components are also first explained. Opportunistic maintenance is applied to the semi-active heave compensation system. Moreover, the joint integrated importance measure (JIIM) between different components at different moments is analyzed and used as the basis for the selection of components on which to perform PM, with the ultimate goal of delaying the degradation of the expected performance of the system. Finally, compared with conditional marginal reliability importance (CMRI)-based OM, the effectiveness of JIIM-based OM is verified by the Monte Carlo method.
Importance measure-based maintenance strategy considering maintenance costs
Maintenance is an important way to ensure the best performance of repairable systems. This paper considers how to reduce system maintenance cost while ensuring consistent system performance. Due to budget constraints, preventive maintenance (PM) can be done on only some of the system components. Also, different selections of components to be maintained can have markedly different effects on system performance. On the basis of the above issues, this paper proposes an importance-based maintenance priority (IBMP) model to guide the selection of PM components. Then the model is extended to find the degree of correlation between two components to be maintained and a joint importance-based maintenance priority (JIBMP) model to guide the selection of opportunistic maintenance (OM) components is proposed. Also, optimization strategies under various conditions are proposed. Finally, a case of 2H2E architecture is used to demonstrate the proposed method. The results show that generators in the 2E layout have the highest maintenance priority, which further explains the difference in the importance of each component in PM.