Objective bayesian analysis for multiple repairable systems

This article focus on the analysis of the reliability of multiple identical systems that can have multiple failures over time. A repairable system is defined as a system that can be restored to operating state in the event of a failure. This work under minimal repair, it is assumed that the failure has a power law intensity and the Bayesian approach is used to estimate the unknown parameters. The Bayesian estimators are obtained using two objective priors know as Jeffreys and reference priors. We proved that obtained reference prior is also a matching prior for both parameters, i.e., the credibility intervals have accurate frequentist coverage, while the Jeffreys prior returns unbiased estimates for the parameters. To illustrate the applicability of our Bayesian estimators, a new data set related to the failures of Brazilian sugar cane harvesters is considered.

Optimizations of Managerial Maintenance Policies

This paper takes up managerial maintenance policies during different phases for mission executions. When a mission execution is divided into two phases and three phases respectively, replacement, minimal repair and keeping failure status become alternatives for managerial maintenance policies. Further, we give approximations of the above managerial maintenance policies to make the computations simple. In this paper, keeping failure status is considered as the last choice for the last phase of mission executions. We aim to minimize the expected maintenance costs for the total mission executions. All of the discussions are made analytically and their numerical examples are given.

Improved Preventive Maintenance Scheduling for a Photovoltaic Plant under Environmental Constraints

The supply of PV power that satisfies the needs of customers is heavily dependent on the reliability of the generating plants. However, irrespective of the robustness of the design of such physical industrial assets, they tend to depreciate with usage and/or age which, in turn, increases the allowance between the design and the operational capabilities. Therefore, to ameliorate the reliability of the system, a combination of selective and preventive maintenance actions were planned by determining the best combination (optimal preventive maintenance intervals, optimal replaced components). In this work, we developed an optimal preventive maintenance strategy with minimal repair using the iterative numerical technique for a PV plant, with and without considering the influence of environmental conditions on the system. An algorithm was developed on MATLAB to determine the optimal number of preventive maintenance actions that yields the maximum availability by selecting the components to be maintained based on the reliability threshold, without considering the environmental impact on the components. The environmental elements’ criticality was introduced, and the reliability reiterated based on the new technique. Finally, by maximizing the availability of the system, an optimal preventive maintenance for a finite horizon was established.

Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime

In this paper, we propose a hierarchical statistical model for a single repairable system subject to several failure modes (competing risks). The paper describes how complex engineered systems may be modelled hierarchically by use of Bayesian methods. It is also assumed that repairs are minimal and each failure mode has a power-law intensity. Our proposed model generalizes another one already presented in the literature and continues the study initiated by us in another published paper. Some properties of the new model are discussed. We conduct statistical inference under an objective Bayesian framework. A simulation study is carried out to investigate the efficiency of the proposed methods. Finally, our methodology is illustrated by two practical situations currently addressed in a project under development arising from a partnership between Petrobras and six research institutes.

An Improved Preventive Maintenance Scheduling for a Photovoltaic Plant under Environmental Constraints

To be able to supply PV power that satisfies customer demand at all times, there is need for the generating plant to be available at all time. However, with the increased age and usage of the components, the component’s reliability reduces resulting to failure. These failures were due to a range of causes such as degradation and electrical wiring aging and cuts leading to a reduced performance efficiency and reliability. Therefore, to ameliorate the reliability of the system, a combined selective and preventive maintenance actions were planned by determining the best combination (optimal preventive maintenance intervals, optimal replaced components). In this work, an optimal preventive maintenance strategy with minimal repair was developed using iterative numerical technique for a photovoltaic (PV) plant with and without considering the influence of environmental condition on the system. An algorithm was developed on MATLAB to determine the optimal number of preventive maintenance actions that yields maximum availability by selecting the components to be maintained based on the reliability threshold without considering the environmental impact on the components. The environmental elements’ criticality was introduced and the reliability reiterated based on the new technique. Finally, by maximizing the availability of the system, an optimal preventive maintenance for a finite horizon was established.

Inspection and maintenance optimisation of multicomponent systems

The present research proposes methodology and mathematical models for optimisation of inspection and maintenance in complex multicomponent systems with finite planning horizon. Components are classified by failure types: hard-type and soft-type. The systems analysed are composed of either multiple identical hidden soft-type components in k-out-of-n redundant configuration, or a combination of hard-type and hidden soft-type components. Failures of hard-type components cause system failures. Failures of components in k-out-of-n systems and soft-type component failures are hidden and not discoverable until an inspection, but reduce the system’s reliability and performance. The systems are inspected either periodically, or non-periodically. They are also inspected opportunistically at the times of system failure (occurring at (k – n + 1)st component failures in k-out-of-n systems, or at hard failures in the systems composed of hard-type and soft-type components). Inspections have negligible duration. All components may undergo minimal repair, or corrective replacement, with hard-type components also having a possibility of preventive replacement under periodic inspections. We only consider minimal repair and corrective replacement under non-periodic inspections. We propose several models for joint optimisation of inspection and maintenance policies that result in the lowest total expected cost. Since soft failures are hidden, we generate expected values for the number of minimal repairs, number of replacements and downtime recursively. Due to multiple component interactions and system complexity, Monte Carlo simulation and genetic algorithms (GA) are used for optimisation. Using GA for optimisation allows to consider quasi-continuous inspection intervals due to improved computational efficiency compared to Monte Carlo simulation. Some of proposed models feature preventive component replacements and are applicable even for systems with hidden component failures. For k-out-of-n systems, we apply periodic model to series and parallel systems and compare the results. We provide expressions for expected number of system failures in terms of cost ratio and component failure intensity. We also provide a simplified expression for system reliability. In addition, we derive a formula for finding the planning horizon length based on expected number of system failures. It may be useful for planning the system’s operating horizon, at the system design stage and when analysing its performance.

Inspection and maintenance optimisation of multicomponent systems

The present research proposes methodology and mathematical models for optimisation of inspection and maintenance in complex multicomponent systems with finite planning horizon. Components are classified by failure types: hard-type and soft-type. The systems analysed are composed of either multiple identical hidden soft-type components in k-out-of-n redundant configuration, or a combination of hard-type and hidden soft-type components. Failures of hard-type components cause system failures. Failures of components in k-out-of-n systems and soft-type component failures are hidden and not discoverable until an inspection, but reduce the system’s reliability and performance. The systems are inspected either periodically, or non-periodically. They are also inspected opportunistically at the times of system failure (occurring at (k – n + 1)st component failures in k-out-of-n systems, or at hard failures in the systems composed of hard-type and soft-type components). Inspections have negligible duration. All components may undergo minimal repair, or corrective replacement, with hard-type components also having a possibility of preventive replacement under periodic inspections. We only consider minimal repair and corrective replacement under non-periodic inspections. We propose several models for joint optimisation of inspection and maintenance policies that result in the lowest total expected cost. Since soft failures are hidden, we generate expected values for the number of minimal repairs, number of replacements and downtime recursively. Due to multiple component interactions and system complexity, Monte Carlo simulation and genetic algorithms (GA) are used for optimisation. Using GA for optimisation allows to consider quasi-continuous inspection intervals due to improved computational efficiency compared to Monte Carlo simulation. Some of proposed models feature preventive component replacements and are applicable even for systems with hidden component failures. For k-out-of-n systems, we apply periodic model to series and parallel systems and compare the results. We provide expressions for expected number of system failures in terms of cost ratio and component failure intensity. We also provide a simplified expression for system reliability. In addition, we derive a formula for finding the planning horizon length based on expected number of system failures. It may be useful for planning the system’s operating horizon, at the system design stage and when analysing its performance.