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Finite state Markov processes and their aggregated Markov processes have been extensively studied, especially in ion channel modeling and reliability modeling. In reliability field, the asymptotic behaviors of repairable systems modeled by both processes have been paid much attention to. For a Markov process, it is well-known that limiting measures such as availability and transition probability do not depend on the initial state of the process. However, for an aggregated Markov process, it is difficult to directly know whether this conclusion holds true or not from the limiting measure formulas expressed by the Laplace transforms. In this paper, four limiting measures expressed by Laplace transforms are proved to be independent of the initial state through Tauber's theorem. The proof is presented under the assumption that the rank of transition rate matrix is one less than the dimension of state space for the Markov process, which includes the case that all states communicate with each other. Some numerical examples and discussions based on these are presented to illustrate the results directly and to show future related research topics. Finally, the conclusion of the paper is given. [ABSTRACT FROM AUTHOR]

It is a well-known definition that the time until entering an absorbing state in a finite state Markov process follows a phase-type distribution. In this article we extend this distribution through adding two new events: one is that the number of transitions among states reaches a specified threshold; the other is that the sojourn time in a specified subset of states exceeds a given threshold. The system fails when it enters the absorbing state or two new events happen, whichever occurs first. We develop three models in terms of three circumstances: (i) the two thresholds are constants; (ii) the number of transitions is random while the sojourn time in the specified states is constant; and (iii) the sojourn time in the specified states is random while the number of transitions is constant. To the performance of such systems, we employ the theory of aggregated stochastic processes and obtain closed-form expressions for all reliability indexes, such as point-wise availabilities, various interval availabilities, and distributions of lifetimes. We select special distributions of two thresholds for models 2 and 3, which are the exponential and geometric distributions. The corresponding formulas are presented. Finally, some numerical examples are given to demonstrate the proposed formulas. [ABSTRACT FROM AUTHOR]

Many practical systems are subject to neglected down time, i.e., if the down time is less than a given threshold, then the system can be considered as working during the down time. Otherwise, the system is considered as in the failure state. This paper investigates the availability and maintenance policy for inspected systems with neglected down time. The instantaneous and steady-state availabilities of the system are derived analytically. Furthermore, a maintenance model is developed to find the optimal inspection interval, , which minimizes the long-run average cost rate. Based on the developed maintenance model, the analytical properties of the optimal inspection policy are explored. Numerical examples on ventilator system calculating the availability and optimal inspection policy are given to illustrate the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

: This paper develops availability and maintenance models for single‐unit systems subject to dependent hard and soft failures. A hard failure stops the system immediately, whereas a soft failure only reduces the performance capacity of the system. Dependence between these 2 types of failures is reflected in the fact that each soft failure directly increases the hazard rate of the hard failure. On the basis of such interaction, we derive recursive equations for the system reliability and availability functions. To detect both types of failures, inspections are executed periodically. Furthermore, we investigate the optimal inspection policy via the minimization of the expected cost per unit time. The applicability of the developed availability and maintenance models is validated by a case study on an electrical distribution system. [ABSTRACT FROM AUTHOR]

The main focus of this paper is on the development of reliability measures for a repairable multi-state system which operates under dynamic regimes under the discrete-time hypothesis. The switching process of regimes is governed by a Markov chain, and the functioning process of the system follows another Markov chain with different transition probability matrices under different regimes. In terms of two chains as above, a new Markov chain is constructed to depict the evolution process of the dynamic system. For the regime consideration, some novel reliability indices are essential and firstly introduced in this paper. By means of hierarchical partitions for the new state space, Ion-Channel modeling theory and discrete-time Markov chain, the traditional and novel reliability and availability functions for the system under random regimes are easily obtained with the closed form solutions, such as two types of system reliabilities, two types of system point availabilities, two types of system multiple-point availabilities and the associated system multi-interval availabilities and so on. In addition, some probability distributions of sojourn times we are interested in are discussed and computed here. Finally, a numerical example is given to illustrate the results obtained in the paper. [ABSTRACT FROM AUTHOR]

To accurately describe the performance of repairable systems operating under alternative environments, for example, mild/harsh, working/idling, maximum/minimum level demand etc., a Semi-Markov process with a finite state space and two different Semi-Markov kernels is introduced. The state set of the system which is regarded as acceptable might depend on the environments. Two important reliability indices, the availability and time to the first system failure, are obtained via Markov renewal theory, transform and matrix methods. The results and numerical examples are also provided for two special cases: (1) when sojourn times under alternative environments are constants and (2) when sojourn times under environments have exponential distributions. [ABSTRACT FROM AUTHOR]

The environment in which a system operates can have a crucial impact on its performance; for example, a machine operating in mild or harsh environments or the flow of a river changing between seasons. In this article, we consider a dynamic reliability system operating under a cycle ofKregimes, which is modeled as a continuous-time Markov process withKdifferent transition rate matrices being used to describe the various regimes. Results for the availability of such a system and probability distributions of the first uptime are given. Three special cases, which occur due to situations where the durations of the regime are constant and where the number of up states in different regimes are identical or increasing, are considered in detail. Finally, some numerical examples are shown to validate the proposed approach. [ABSTRACT FROM PUBLISHER]

Availability is an important measure of performance for repairable systems because it can describe the probability of a repairable system being in working states. Single point and interval availability measurements (availabilities) have been widely used, but they cannot meet the requirements of all applications. In this paper, multi-point, multi-interval, and mixed multi-point-interval availabilities are introduced, which are extensions of the single point and interval availabilities. The multi-interval availability formula was given by Csenki in 2007 by using the induction method in which the multi-interval availability was called the joint interval reliability, but a new simple technique is provided to present the formula of multi-interval availability in this paper. We also consider the steady-state situations for all new availabilities. For Markov repairable systems, the calculation formulae are presented for the new availabilities, and some properties of new availabilities are discussed too. Finally, some special cases and numerical examples are given to illustrate the results obtained in the paper. [ABSTRACT FROM AUTHOR]

This article analyzes a single-unit repairable system consisting of an operating subsystem and a maintenance subsystem that is solely used to repair the operating subsystem in the event of it breaking down. Formulae for reliability indexes such as availability and distributions concerned with visits to certain subsets of states are presented in terms of state aggregations in which two partitions are used. The situation with exponentially distributed operational times is discussed not only as a special case of the approach used in this article but also using a Markov model. A numerical example is given to illustrate the results obtained in this article. [ABSTRACT FROM PUBLISHER]

The dynamics of a system represented by a finite-state Markov process operating under two alternating regimes, for example, day/night, machine working/machine idling, etc., are modeled in this article. The transition rate matrices under the two regimes will usually be different. Also, the set of states of the system that are regarded as satisfactory may depend on the regime in operation: for example, a particular state of the system that may be regarded as satisfactory by day might not be tolerated at night (e.g., the headlights on a car not working). It is assumed that the regime durations are random variables and results are obtained for the availability of such a system and probability distributions for uptimes. Results and numerical examples are also given for two special cases: (i) when the regimes are of fixed duration; and (ii) when the regime durations have negative exponential distributions. [ABSTRACT FROM AUTHOR]

A new system is defined based on a series Markov repairable system. In this new system, if a repair time of the system failure is too short (less than a given critical value) to cause the system to fail, then the repair interval may be omitted from the downtime record, i.e., the failure effect could be neglected. Otherwise, if a repair time is longer than the given critical value, then the system remains operating from the beginning of this repair until this repair time exceeding the critical value, i.e., the failure effect could be delayed. In Ion-Channel modeling, we call this situation the time interval omission problem. Incorporating this situation, the availability indices are presented as a measure of reliability. Some numerical examples are shown to illustrate the results obtained in the paper. [ABSTRACT FROM PUBLISHER]

This paper introduces a Markov model for a multi-state repairable system in which some states are changeable in the sense that whether those physical states are up or down depends on the immediately preceding state of the system evolution process. Several reliability indexes, such as availability, mean up time, and steady-state reliability are calculated using the matrix method. A sufficient condition under which the availabilities of the stochastically monotone repairable systems with history-dependent states can be compared is also obtained. Some examples are presented to illustrate the results in the paper. [ABSTRACT FROM AUTHOR]

• A novel concept of repairable system's balanced condition is proposed. • The PH-distributions have been considered for Model 1. • Three effective maintenance policies are provided for case II of Model 2. • System reliability and availability indexes are derived for the two models. This paper considers a novel k -out-of- n : F repairable balanced system with m sectors. The characteristic of this balanced system is that the numbers of working components in the m sectors must keep the same at any time. Thus, some components should be shut down manually when the balance is broken. Specifically, two Markov repairable system models are proposed in this paper in terms of different restart mechanisms of shut down components. The excellent Markov process imbedding method is used to obtain the system reliabilities and availabilities. Phase-type (PH) distributions for the lifetime and repair time of the components in the first model are considered. For the second case of the second model, three different maintenance policies are proposed and their performances are compared through several numerical examples. To the best of our knowledge, the k -out-of- n : F repairable balanced system models proposed in the research of this paper are first reported in literature. Finally, numerical examples are given to illustrate the models and the approaches. [ABSTRACT FROM AUTHOR]

Markov history-dependent repairable systems refer to the Markov repairable systems in which some states are changeable and dependent on recent evolutional history of the system. In practice, many Markov history-dependent repairable systems are subjected to neglected failures, i.e., some failures do not affect system performances if they can be repaired promptly. In this paper, we develop a model based on the theory of aggregated stochastic processes to describe the history-dependent behavior and the effect of neglected failures on the Markov history-dependent repairable systems. Based on the developed model, instantaneous and steady-state availabilities are derived to characterize the reliability of the system. Four reliability-related time distributions, i.e., distribution for the k th working period, distribution for the k th failure period, distribution for the real working time in an effective working period, distribution for the neglected failure time in an effective working period, are also derived to provide a more comprehensive description of the system's reliability. Thanks to the power of the theory of aggregated stochastic processes, closed-form expressions are obtained for all the reliability indexes and time distributions. Finally, the developed indexes and analysis methods are demonstrated by a numerical example. [ABSTRACT FROM AUTHOR]