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The purpose of this paper is to model the occurrences of successive failure types and times to failure of the two repairable machine systems.

Historical data on failure types and time to failures of the given machine systems (4 nos) were gainfully used. Second order time homogeneous Markov Chain models were used to characterize the occurrences of the two broad failure type, namely, mechanical and electrical, after having found that the occurrences of failure types were dependent. Second order time homogeneous Markov Chain with Bivariate Distribution function (M2BVD) was used to model the times between successive failures {T_n, n≥1} for each machine system.

It is possible to apply the theoretical framework of Markov chain models to the accumulated data on failure types and failure times of any repairable system, which provide a wealth of information on the systems and are often left unused.

The framework used in the study can be improved to accommodate multiple failure types and failure times of any repairable system to the extent that a more accurate prediction of these two variables and a better estimate of the system reliability are available.

The models for failure types and failure times of the given machine systems would be of immense use to the maintenance crew for predicting the future failure types and failure times of any given system and subsequently organizing and fine‐tuning their state of preparedness.

Repairable system reliability analysis is very important to industry and, for complex systems, replacing a failed component is the most commonly used corrective maintenance action as it is an inexpensive way to restore the system to its functional state. However, failure data analysis for repairable system is not an easy task and usually a number of assumptions which are difficult to validate have to be made. Despite the fact that time series models have the advantage of few such assumptions and they have been successfully applied in areas such as chemical processes, manufacturing and economics forecasting, its use in the field of reliability prediction has not been that widespread. In this paper, we examine the usefulness of this powerful technique in predicting system failures. Time series models are statistically and theoretically sound in their foundation and no postulation of models is required when analysing failure data. Illustrative examples using actual data are presented. Comparison with the traditional Duane model, which is commonly used for repairable system, is also discussed. The time series method gives satisfactory results in terms of its predictive performance and hence can be a viable alternative to the Duane model.

The purpose of this paper is to provide comprehensive information on preventive maintenance (PM) planning and methods used in the industry in order to achieve an effective maintenance system.

The literature review is organized in a way that provides the general overview of the researches done in the PM. This paper discusses the literatures that had been reviewed on four main topics, which are the holistic view of maintenance policies, PM planning, PM planning concept and PM planning-based in developing optimal planning in executing PM actions.

PM policy is one of the original proactive techniques that has been used since the start of researches on maintenance system. Review of the methods presented in this paper shows that most researches analyse effectiveness using artificial intelligence, simulation, mathematical formulation, matrix formation, critical analysis and multi-criteria method. While in practice, PM activities were either planned based on cost, time or failure. Research trends on planning and methods for PM show that the variation of approaches used over the year from early 1990s until today.

Research about PM is known to be extensively conducted and majority of companies applied the policy in their production line. However, most analysis and method suggested in published literatures were done based on mathematical computation rather than focussing on solution to real problems in the industry. This normally would lead to the problems in understanding by the practitioner. Therefore, this paper presented researches on PM planning and suggested on the methods that are practical, simple and effective for application in the real industry.

The originality of this paper comes from its detail analysis of PM planning in term of its research focus and also direction for application. Extensive reviews on the methods adopted in relation to PM planning based on the planning-based such as cost-based, time-based and failure-based were also provided.

The purpose of this paper is to investigate the determinants of the timing of bank failure in North Cyprus over the period of 1984‐2002 using a discrete‐time logistic survival analysis.

The empirical methodology employed in the paper allows for the determination of the factors that influence the time to bank failure. The model links the time of bank failure to a set of bank‐specific factors and macro‐environment that may have exacerbated the internal troubles of the financial institutions.

An empirical examination of the results on survival analysis reveal that the three variables, namely: low asset quality (total loan as a percentage of total assets), low liquidity (total liquid asset as a percentage of total assets), and high credit extended to the private sector (ratio of the private credit to gross domestic product) are the main factors that explain the survival time of banks in North Cyprus.

For further research this paper may better distinguish time to bank failure if it extends the time period and if it uses exchange pressure from Turkey that may have a direct effect on bank failure in North Cyprus.

Nowadays bank failure is an important problem in the world. Using time technique to investigate bank failure will help to learn the factors that determine time to bank failure, which will further help to take precautions and prevent the cost of bank failure.

The analysis would appear to be the first to provide evidence and investigate the time to bank failure in the North Cyprus banking sector.

Considers a system (machine) that is subject to failure (breakdown). Two characterizations are presented. In the first characterization, the state of the system is described by the real age of the machine and the number of failures incurred to date. In the second characterization, the state of the system is described by the real age of the machine and the virtual age of the machine. In either characterization, upon failure, the unit may undergo a repair which can partially reset the failure intensity of the unit. The degree of reset assumed by the repair is a function of the characterization utilized. The other alternative, at a failure, is to conduct a major overhaul that serves to refresh the failure intensity of the unit. General cost structures, depending upon (real age, number of failures) in characterization one or (real age, virtual age) in characterization two are permitted. The decision, on failure to repair or renew is formulated as a discrete semi‐Markov Decision process. Optimal decisions are of the threshold type. The threshold rules depend upon the characterization.

Industry decision makers often rely on a risk-based approach to perform inspection and maintenance planning. According to the Risk-Based Inspection and Maintenance Procedure project for the European industry, risk has two main components: probability of failure (PoF) and consequence of failure (CoF). As one of these risk drivers, a more accurate estimation of the PoF will contribute to a more accurate risk assessment. Current methods to estimate the PoF are either time-based or founded on expert judgement. This paper suggests an approach that incorporates the proportional hazards model (PHM), which is a statistical procedure to estimate the risk of failure for a component subject to condition monitoring, into the risk-based inspection (RBI) methodology, so that the PoF estimation is enhanced to optimize inspection policies.

To achieve the overall goal of this paper, a case study applying the PHM to determine the PoF for the real-time condition data component is discussed. Due to a lack of published data for risk assessment at this stage of the research, the case study considered here uses failure data obtained from the simple but readily available Intelligent Maintenance Systems bearing data, to illustrate the methodology.

The benefit of incorporating PHM into the RBI approach is that PHM uses real-time condition data, allowing dynamic decision-making on inspection and maintenance planning. An additional advantage of the PHM is that where traditional techniques might not give an accurate estimation of the remaining useful life to plan inspection, the PHM method has the ability to consider the condition as well as the age of the component.

This paper is proposing the development of an approach to incorporate the PHM into an RBI methodology using bearing data to illustrate the methodology. The CoF estimation is not addressed in this paper.

This paper presents the benefits related to the use of PHM as an approach to optimize the PoF estimation, which drives to the optimal risk assessment, in comparison to the time-based approach.

Suggests new ways to construct and update preventive schedules for a complex system by making better use of system failure down time to do preventive work without further productive loss. The methodology is based on age renewal but necessarily approximate. However, it includes the recursive effects of maintenance on system MTBF, and of opportunities insufficient to prevent system deterioration. Opportunity maintenance theoretically self‐adjusts; if insufficient opportunities arise, average lateness increases, and failures increase until a balance is achieved, but minimum conditions exist for a given age renewal schedule, and the natural balance may not be economically optimal.

The purpose of this paper is to provide the maintenance personnel with a methodology for modeling and estimating the reliability of critical machine systems using the historical data of their inter‐failure times.

The failure patterns of five different machine systems were modeled with NHPP‐log linear process and HPP belonging to stochastic point process for predicting their reliability in future time frames. Besides the classical approach, Bayesian approach was also used involving Jeffreys's invariant non‐informative independent priors to derive the posterior densities of the model parameters of NHPP‐LLP and HPP with a view to estimating the reliability of the machine systems in future time intervals.

For at least three machine systems, Bayesian approach gave lower reliability estimates and a larger number of (expected) failures than those obtained by the classical approach. Again, Bayesian estimates of the probability that “ROCOF (rate of occurrence of failures) would exceed its upper threshold limit” in future time frames were uniformly higher for these machine systems than those obtained with the classical approach.

This study indicated that, the Bayesian approach would give more realistic estimates of reliability (in future time frames) of the machine systems, which had dependent inter‐failure times. Such information would be helpful to the maintenance team for deciding on appropriate maintenance strategy.

With the help of Bayesian approach, the posterior densities of the model parameters were found analytically by considering Jeffreys's invariant non‐informative independent prior. The case study would serve to motivate the maintenance teams to model the failure patterns of the repairable systems making use of the historical data on inter‐failure times and estimating their reliability in future time frames.

The purpose of this study is to propose the time series decomposition approach to analyze and predict the failure data of the repairable systems.

This study employs NHPP to model the failure data. Initially, Nelson's graph method is employed to estimate the mean number of repairs and the MCRF value for the repairable system. Second, the time series decomposition approach is employed to predict the mean number of repairs and MCRF values.

The proposed method can analyze and predict the reliability for repairable systems. It can analyze the combined effect of trend‐cycle components and the seasonal component of the failure data.

This study only adopts simulated data to verify the proposed method. Future research may use other real products' failure data to verify the proposed method. The proposed method is superior to ARIMA and neural network model prediction techniques in the reliability of repairable systems.

Results in this study can provide a valuable reference for engineers when constructing quality feedback systems for assessing current quality conditions, providing logistical support, correcting product design, facilitating optimal component‐replacement and maintenance strategies, and ensuring that products meet quality requirements.

The time series decomposition approach was used to model and analyze software aging and software failure in 2007. However, the time series decomposition approach was rarely used for modeling and analyzing the failure data for repairable systems. This study proposes the time series decomposition approach to analyze and predict the failure data of the repairable systems and the proposed method is better than the ARIMA model and neural networks in predictive accuracy.

The subject of investigation reported in this paper is the determination of an optimal replacement time for equipment that deteriorates with time. The following hypothesis is proposed and investigated. While a piece of equipment is in the final stages of its life span, i.e. the wear‐out phase, the application of preventive replacement strategy at constant time intervals reduces total down‐time. The novelty of the approach used in this research lies in the conversion of the more complicated classical constant‐interval replacement model to a simplified but nonetheless effective model. Results are shown for a case where the equipment time‐to‐failure has a normal distribution. These results also hold for a Weibull distribution with known shape and scale parameters. The simplified methods proposed in this paper can assist maintenance managers to better make economic decisions about equipment maintenance.