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In this paper, a maintenance strategy based on improved imperfect maintenance actions with stochastic repair times for multiperiod randomly failing equipment is developed. The main objective is to minimize the total maintenance cost by jointly finding the optimal preventive maintenance (PM) cycle and planning horizon.

A model based on the mathematical theory of reliability is developed to minimize the total maintenance cost by jointly finding the optimal couple: PM cycle T* and planning horizon H*. The proposed model aims to characterize the evolutionary impact of imperfect PM actions on the equipment failure rate and the resulting mean number of failures. The conventional threshold accepting (TA) algorithm is implemented to solve the proposed model. A numerical example for a given set of input parameters is presented in order to show the usefulness of the proposed model. A sensitivity analysis of some of the key parameters is performed to demonstrate the coherence of the developed maintenance policy.

The obtained results showed a sensitive trade-off between PM frequency and the total maintenance cost. Performing PM actions more frequently helps significantly to reduce the expected number of corrective maintenance actions and the corresponding total cost. It has also been found that improving the efficiency of the PM actions allows for maintaining the equipment less frequently by increasing the time between successive PM actions.

Given the complexity of the objective function to be minimized and the stochastic nature of the model's parameters, the authors limited this study to equally cyclic production periods over the planning horizon.

The present model aims to provide an integrated maintenance/production comprehensive framework to assist planners in establishing maintenance schedules considering multiperiod randomly failing production systems and the evolutionary impact of imperfect PM actions on the equipment failure rate.

Contrary to the majority of existing works in the literature dealing with maintenance strategies, the authors consider that repair times are stochastic to provide a more realistic framework. In addition, the developed model considers the impact of imperfect maintenance on the equipment's mean time to failure. Thus, the evolutionary impact of imperfect PM actions on the equipment failure rate and the resulting mean number of failures is characterized. Simultaneously, the production planning horizon along with the length of each PM cycle is optimized in order to minimize the total maintenance cost over the planning horizon.

Temperature cycling tests, and statistical analysis of the results, for various high‐density packages on printed‐circuit boards with Sn‐Cu hot‐air solder levelling, electroless nickel‐immersion gold, and organic solder preservative finishes are investigated in this study. Emphasis is placed on the determination of the life distribution and reliability of the lead‐free solder joints of these high‐density package assemblies while they are subjected to temperature cycling conditions. A data acquisition system, the relevant failure criterion, and the data extraction method will be presented and examined. The life test data are best fitted to the Weibull distribution. Also, the sample mean, population mean, sample characteristic life, true characteristic life, sample Weibull slope, and true Weibull slope for some of the high‐density packages are provided and discussed. Furthermore, the relationship between the reliability and the confidence limits for a life distribution is established. Finally, the confidence levels for comparing the quality (mean life) of lead‐free solder joints of high‐density packages are determined.

The purpose of this paper is to emphasize that the choice of the most appropriate maintenance model and policies is the best way to reduce significantly the maintenance costs as well as to optimize the useful Key Performance Indicators – failure rates, reliability, mean time between failures, mean time to repair, and equipment availabilities.

In order to implement the Asset Effectiveness Optimization AEO as well as the Overall Equipment Effectiveness OEE, improving productivity in a complex food‐products plant, the paper presents a theoretical and experimental study related to the maintenance costs directly associated with the equipment used in production tasks.

The developed tool is an efficient method of calculating the maintenance costs and allows one by means of computational simulation to define the most advisable maintenance policy. On the other hand, the proposed relationships are universal and could be used as an economic evaluation indicator for other industries and equipment.

Further research should include the application of the proposed methodology to the similar equipment of other food‐products plants as well as to other different equipment in order to create benchmarking procedures. This generator of technical information is the most appropriate method of optimizing maintenance key performance indicators.

As is well known, equipment availability must be as close to 100 per cent as possible, in order to avoid non‐planned breakdowns with the consequent production losses. Then it is important to adopt the most advisable maintenance policies and practices, the proposed methodology being an efficient tool for evaluating the maintenance performance and, in addition, for optimizing procedures.

The proposed methodology represents an efficient way to evaluate the maintenance performance as well as to choose better maintenance policies and practices in order to reduce costs and increase maintenance key performance indicators.

The purpose of this paper is to identify the critical components of a complex system by using survival signature. First, a complex system is abstracted with varying scales and generates a multi-levels model. Then reliability evaluations can be conducted by survival signature from rough to fine for tracing and identifying them. Finally, the feasibility of the proposed approach is demonstrated by an actual production system.

The paper mainly applies a multi-level evaluating strategy for the reliability analysis of complex systems with components of multiple types. In addition, a multi-levels model of a complex system is constructed and survival signature also used for evaluation.

The proposed approach was demonstrated to be the feasibility by an actual production system that is used in the case study.

The case study was performed on a system with simple network structure, but the proposed approach could be applied to systems with complex ones. However, the approach to generate the digraphs of abstraction levels for complex system has to be developed.

So far the approach has been used for the reliability analysis of a machining system. The approach that is proposed for the identification of critical components also can be applied to make maintenance decision.

The multi-level evaluating strategy that was proposed for reliability analysis and the identification of critical components of complex systems was a novel method, and it also can be applied as index to make maintenance planning.

Downtime is a process parameter that substantially impacts on the operating hours and results in production losses, thus motivating maintenance engineers to control process plants. Notwithstanding, the impacting nature of process equipment failure on the operating hours in bottling plants remains inadequately examined. In this paper, the cause-and-effect analysis was used to establish the root cause of the downtime problem and Pareto analysis employed to justify the greatest opportunities for improvement in reducing downtime and increasing reliability levels. Weibull analysis is then conducted on the industrial setting. Novel aspect ratios are proposed.

Using the Weibull failure function of machines as a principal facilitator to produce failure predictions, the downtime behaviour of a process plant was modelled and tested with practical data from a bottling process plant. This research was conducted in a Nigerian process bottling plant where historical data were examined.

The analysis of the results shows the following principal outcome: First, the machines with the highest and least downtime values are 2 and 5, respectively, with correspondingly mean values of 22.83 and 4.39 h monthly. Second, the total downtime 92.05 and 142.14 h for the observed and target downtime, with a coefficient of determination of 0.5848 was recorded. Third, as month 1 was taken as the base period (target), all the machines, except M5 had accepted performance, indicating proper preventive maintenance plan execution for the bottling process plant. Availability shows a direct relationship between the failure and uptime of the machines and the downtime impacts on production. Two machines had random failure pattern and five machines exhibited a wear-out failure pattern and probably due to old age and wear of components in the machines.

The major contribution of the paper is the Weibull modelling in a unique application to a bottling plant to avoid current practices that use reliability software that is not easily accessible.

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.

For manufacturing systems which are in continuous operation and subject to breakdown, inspection can be an appropriate maintenance strategy. In this situation, inspection can reduce down‐time and increase system reliability. In this paper two main ideas are proposed. In the first, an inspection effect function is introduced which modifies the traditional system failure rate distribution. This modification involves a formula which demonstrates the effect of inspection frequency and inspection effectiveness on system failure rate distribution. It is then argued that under inspection policy the system’s traditional failure rate is necessarily affected by these factors. The second idea presents a maintenance model in which the system is interrupted in its time to failure by inspections. Optimisation of this model determines an optimal inspection frequency which minimises the system’s total down‐time. Thus, it is shown that by optimising inspection frequency system availability can be increased.

Studies a non‐homogeneous Poisson process software reliability model with failure rate based on Zipf’s law. Discusses the rate function, mean value function and the estimation of parameters. The proposed model can be used to analyse the reliability growth. The results of applying the proposed model and Duane model to several actual failure data sets show that the model with failure rate observed from Zipf’s law can fit not only in operating software but also in testing software. The result also indicates that the proposed model has better long‐term predictive capability than the Duane model for failure data sets with power law’s failure rates

The aim of this paper is to propose efficient models and algorithms for reliability value analysis of complex repairable systems linking reliability and losses from failures.

The conventional reliability analysis is based on the premise that increasing the reliability of a system will decrease the losses from failures. In this paper it is demonstrated that a system with larger reliability does necessarily mean a system with smaller losses from failures. In other words, a system reliability improvement, which is disconnected from the losses from failures does not necessarily reduce the losses from failures. An efficient discrete‐event simulation model and algorithm are proposed for tracking the losses from failures for systems with complex topology. A new algorithm is also proposed for system reliability analysis related to productions systems based on multiple production units where the absence of a critical failure means that at least m out n production units are working.

A model for determining the distribution of the net present value (NPV) characterising the production systems is developed. The model has significant advantages compared to models based on the expected value of the losses from failures. The model developed in this study reveals the variation of the NPV due to variation of the number of critical failures and their times of occurrence during the entire life‐cycle of the systems.

The proposed models have been successfully applied and tested for reliability value analysis of productions systems in deepwater oil and gas production.

The proposed approach has been demonstrated by comparing the losses from failures and the NPVs of two competing design topologies: one based on a single‐channel control and the other based on a dual‐channel control.

The physical and mathematical definitions, as well as the practical significance, of quality and reliability are explained, with particular regard to surface mount electronic components and assemblies. The validity of accelerated assessments of long‐term service performance is also discussed.