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The move toward Industry 4 is accelerated by the availability of affordable sensing technologies and networking infrastructure. Condition-Based Maintenance is the well-suited maintenance strategy to make use of the information available on assets condition to optimize maintenance interventions. However, devising the optimum maintenance policy requires a representative model of the maintenance system. Most of the existing research has been focusing on single-component systems. However, assets nowadays are complex and composed of many components. The modeling of multicomponent maintenance systems presents various challenges, especially if interactions between components, such as stochastic, structural, economic and resource dependencies are considered.

In this paper, we present a detailed modeling approach based on Discrete Event Simulation for nonidentical two-component systems subject to Condition-Based Maintenance considering all four types of dependencies.

The research has shown that optimizing the maintenance system without considering resource dependence led to different and better solutions. In addition, there is a trade-off between maintenance cost and asset availability, confirming the need for multiobjective optimization.

This paper outlines a modeling approach of CBM for nonidentical two-component systems considering stochastic, structural, economic and resource dependencies. A demonstration on a case study is followed where multiobjective optimization was applied to obtain the optimal maintenance policy while minimizing maintenance cost and maximizing asset availability simultaneously.

The purpose of this paper is the reliability analysis for systems with dependent gamma degradation process and Weibull failure time.

Consider a life testing experiment in which a sample of n devices starts to operate at t=0 and the data are available on failure time and failure-evolving process on each individual, called in some contents wear or degradation. Ignoring the between performance characteristics dependency structure may lead us to different reliability estimations, while the dependency justly exists. In previous research, dependency between the degradation process and hard failure time has been studied in limited detail (special closed form expression). Thereafter, the dependency between two degradation processes with the same structure (gamma process) in a system is considered using the copula function.

The results indicate that ignoring the dependency structure may lead us to different reliability estimations while the dependency justly exists.

This study gives some contributions that evaluate reliability metrics with more than one failure mechanism that may not be independent and possibly follow a different distribution function. The authors have used the copula function as a basis to develop a proposal model and analysis methods. In addition, the authors discussed the identifiability of the copula. Finally, simulation data were used to review the suggested approach.

The purpose of this paper is to study the risk of misspecifying solvency models for insurance companies.

Based on a basic solvency model, the authors examine the sensitivity of different risk measures with respect to model misspecification. An analysis considers the effects of introducing stochastic jumps and linear, as well as non‐linear dependencies into the basic setting on the solvency capital requirements, shortfall probability and expected policyholder deficit. Additionally, the authors take a regulatory view and consider the degree to which the deviations in risk measures, due to the different model specifications, can be diminished by means of requiring interim financial reports.

The simulation results suggest that the sensitivity of solvency capital as a risk measure – as it is in regulatory practice – underestimates the actual misspecification risk that policyholders are exposed to. It is also found that semi‐annual mandatory interim reports can already reduce the model uncertainty faced by a regulator, significantly. This has important implications for the design of risk‐based capital standards and the implementation of internal solvency models.

The results from the Monte Carlo simulation show that changes in the specification of a solvency model have a much greater impact on shortfall probabilities and expected policyholder deficits than they have on capital requirements. The shortfall risk measures react much more sensitively to small changes in the model assumptions, than the capital requirements. This leads us to the conclusion that regulators should not solely rely on capital requirements to monitor the solvency situation of an insurer, but should additionally consider shortfall risk measures. More precisely, an analysis of model risk focusing on the sensitivity of capital requirements will typically underestimate the relevant risk of model misspecification from a policyholder's perspective. Finally, the simulation results suggest that mandatory interim reports on the solvency and financial situation of an insurance company are a powerful tool in order to reduce the model uncertainty faced by regulators.

Violent geo-political conflicts are on the rise across the globe, particularly within fragile states. Using path-dependency theory, this paper aims to explore Fiji in the context of its public administrative history examining the legacies of history that have contributed to its ongoing conflicts.

An archival document analysis along with a theoretical thematic analysis was used to collect and assess data. Themes were identified that explain how and when the conflict became path-dependent.

Analysing conflict as path-dependent demonstrates how indirect rule while Fiji was under colonial rule, and the short time it has taken for the nation to transition from a colony to an independent State contributed to the eruption of conflicts in Fiji.

The research makes two key contributions, namely, it develops a theoretical understanding of conflict using path-dependency theory and it uncovers legacies of colonialism that have shaped conflict in Fiji.

This paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar approaches. The authors provide a theoretical justification and extensive backtesting of our approach.

The authors theoretically derive a scenario-based value-at-risk for interest rate risks based on a principal component analysis. The authors calibrate their approach based on the Nelson–Siegel model, which is modified to account for lower bounds for interest rates. The authors backtest the model outcomes against historical yield curve changes for a large number of generated asset–liability portfolios. In addition, the authors backtest the scenario-based value-at-risk against the stochastic model.

The backtesting results of the adjusted Nelson–Siegel model (accounting for a lower bound) are similar to those of the traditional Nelson–Siegel model. The suitability of the scenario-based value-at-risk can be substantially improved by allowing for correlation parameters in the aggregation of the scenario outcomes. Implementing those parameters is straightforward with the replacement of Pearson correlations by value-at-risk-implied tail correlations in situations where risk factors are not elliptically distributed.

The paper assumes deterministic cash flow patterns. The authors discuss the applicability of their approach, e.g. for insurance companies.

The authors’ approach can be used to better communicate interest rate risks using scenarios. Discussing risk measurement results with decision makers can help to backtest stochastic-term structure models.

The authors’ adjustment of the Nelson–Siegel model to account for lower bounds makes the model more useful in the current low-yield environment when unjustifiably high negative interest rates need to be avoided. The proposed scenario-based value-at-risk allows for a pragmatic measurement of interest rate risks, which nevertheless closely approximates the value-at-risk according to the stochastic model.

Financial firms announcing large operational losses have empirically been shown to cause significant negative spillover effects in other non-announcing firms in case of the banking and insurance industry. The purpose of this paper is 1) to model such spillover effects in a network from a portfolio perspective and 2) to holistically assess operational risk, reputational risk and the risk of spillover effects, taking into account the dependencies between these risk types.

The authors propose different approaches to model spillover effects with different complexity, including stochasticity and influencing factors within the industry network. They then calibrate the model based on information from previous empirical literature.

The results emphasize that spillover effects can represent a considerable (non-diversifiable) risk, especially in portfolios, and that neglecting them may lead to a severe underestimation of the actual impact of single operational loss events.

This study is relevant not only for a firm’s risk management strategy but also for investors holding a portfolio of firms potentially subject to spillover effects.

Opportunistic maintenance (OM) policy is a prospective maintenance approach that instigates for a more effective and optimized system. The purpose of this paper is to provide the steps and methods used in model development processes for the application of the OM policy.

Dubbed as opportunistic principle toward optimal maintenance system (OPTOMS) for OM policy toward optimal maintenance system, the model is devised as a decision support system model and contains five phases. The motivation and focus of the model resolve around the need for a practical framework or model of maintenance policy for the application in an industry. In this paper, the OPTOMS model was verified and validated to ensure that the model is applicable in the industry and robust as a support system in decision making for the optimal maintenance system.

From the verification steps conducted in a case study company, it was found that the developed model incorporated simple but practical tools like check sheet, failure mode and effect analysis (FMEA), control chart that has been commonly used in the industry.

This paper provides the general explanations of the developed model and tools used for each phase in implementing OM to achieve an optimal maintenance system. Based on a case study conducted in a semiconductor company, the OPTOMS model can align and prepare the company in increasing machine reliability by reducing machine downtime.

The novelty of this paper is based on the in-depth discussion of all phases and steps in the model that emphasize on how the model will become practical theories in conducting an OM policy in a company. The proposed methods and tools for data collection and analysis are practical and commonly used in the industry. The framework is designed for practical application in the industry. The users would be from the Maintenance and Production Department.

Improving reliability is a key factor in reducing the cost of wind energy, which is strongly influenced by the cost of maintenance operations. In this context, this paper aims to propose a degradation model that describes the phenomenon of fault propagation to apply proactive maintenance that will act on the cause of failure to prevent its reoccurrence as well as to improve future system designs.

The methodology adopted consists in identifying the different components of a wind turbine, their causes and failure modes, and then, classifying these components according to their causes of failure.

The result is a classification of the different components of a wind turbine according to their failure causes. From the obtained classification, the authors observed that the failure modes for one component are a failure cause for another component, which describes the phenomenon of failure propagation.

The different classifications existing in the literature depend on the nature, position and function of the different components. The classification of this study consists in grouping the components of a wind turbine according to their failure causes to develop a degradation model considering the propagation of failure in the field of wind turbines.

This paper aims to deal with the problems of failure dependence and common cause failure (CCF) that arise in reliability analysis of complex systems.

Firstly, a dynamic fault tree (DFT) is used to capture the dynamic failure behaviours and converted into an equivalent generalized stochastic petri net (GSPN) for quantitative analysis. Secondly, an efficient decomposition and aggregation (EDA) theory is combined with GSPN to deal with the CCF problem, which exists in redundant systems. Finally, Birnbaum importance measure (BIM) is calculated based on the EDA approach and GSPN model, and it is used to take decisions for system improvement and fault diagnosis.

In this paper, a new reliability evaluation method for dynamic systems subject to CCF is presented based on the DFT analysis and the GSPN model. The GSPN model is easy to capture dynamic failure behaviours of complex systems, and the movement of tokens in the GSPN model represent the changes in the state of the systems. The proposed method takes advantage of the GSPN model and incorporates the EDA method into the GSPN, which simplifies the reliability analysis process. Meanwhile, simulation results under different conditions show that CCF has made a considerable impact on reliability analysis for complex systems, which indicates that the CCF should not be ignored in reliability analysis.

The proposed method combines the EDA theory with the GSPN model to improve the efficiency of the reliability analysis.

This paper reviews the literature on opportunistic maintenance (OM) as new advance maintenance approach and policy. The purpose of this paper is to conceptually identify common principle and thereby provide absolute definition, concept and characteristics of this policy.

A conceptual analysis was conducted on various literatures to clarify a number of principle and concepts as a method for understanding information on OM. The analysis involves the process of separating the compound terms used in the literatures into a few parts, analyse them and then recombining them to have more clear understanding of the policy.

The paper discussed the maintenance approach, genealogy, principle, concept and applications of OM both in numerical analysis and real industry. OM policy is developed based on combination of age replacement policy and block replacement policy and in practical; OM is applied as the combination of corrective maintenance which is applied when any failure occurred, with preventive maintenance (PM) – a planned and scheduled maintenance approach to prevent failure to happen. Any machine shutdown or stoppages due to failure is the “opportunity” to conduct PM even though it is not as planned. The characterization of OM was provided in order to present its theoretical novelty for researchers and practical significance for industries.

To date, there is no publication that reviews the OM in-depth and provides clear understanding on the topic. Therefore, this paper aims to show lineage of OM and the current trend in researches. This discussion will pave the way of new research areas on this optimal maintenance policy. Clear definition and principle of OM provided in this paper will trigger interest in its practicality as well as aid industries to understand and conduct OM in operation plant.

This paper discussed the available literature about OM in various perspectives and scopes for further understanding of the topic by maintenance management professionals and researchers. Therefore, OM can be widely studied and applied in real industry as it is an effective and optimal maintenance policy.