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The purpose of this paper is to present models and practical procedures for the analysis of maintenance operations and development of maintenance policies in the context of an oil filling factory. Basic maintenance models, maintenance policy selection and optimum spare part quantity determination procedures are illustrated with a specific case application in a factory.

Maintenance formulas are constructed and applied in a factory to determine down time due to various types of failures and maintenance practices in the system. Based on the analysis of the current system, a new preventive maintenance policy is proposed and its effect on reducing down time due to random failures is estimated by using the formulation developed. Procedures for spare part requirements and optimum order quantities with respect to total costs are outlined for critical spares.

Models and case study results presented in this paper demonstrate that selection of appropriate maintenance policy and optimum spare part order quantities should be based on scientific procedures since the results can significantly affect system performance.

The results obtained in this paper are specific to the case application. However, the models and procedures presented are general and can be applied to any similar problems.

Formulations and procedures outlined in this paper can be used to determine effects of various types of maintenance activities and related policies on system performance. They can be valuable tools for maintenance engineers and operational managers in improving system productivity.

The paper considers a management problem related to maintenance policy selection and spare part order quantity determination in a factory. Detailed procedures outlined in this paper can be highly valuable tools for maintenance managers.

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.

This paper is the outcome of a study carried out in a nine‐cell flexible manufacturing unit which produces components for Diesel engine injection pumps. The study involved the monitoring and recording of the various types of failure which led to interruptions in production. The objective of the study was to design a maintenance system for the optimal management of replacement stocks, both from the technical and economic point of view. The design of this programme was based on matching a given statistical distribution (Weibull’s distribution, exponential, normal, log‐normal, or gamma) to the time between failures. By means of this fit, the basic system parameters were determined: average lifetime, failure rate, mean time between failures, mean time between inspections. Knowledge of these variables permits us to determine the frequency of inspections in relation to the level of reliability we require of the system. This information also enables us to establish a programme to determine which elements require inspection, the frequency of these inspections and the operations that need to be carried out.

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.

Degradation of repairable components may not be similar after each maintenance activity; thus, the classic (traditional-time based) maintenance policies, which consider preventive maintenance (PM), age-based maintenance and overhauls to be done at fixed time interval, may fail to monitor the exact condition of the component. Thus, a progressive maintenance policy (PMP) may be more appropriate for the industries that deal with large, complex and critical repairable systems (RS) such as aerospace industries, nuclear power plants, etc.

A progressive maintenance policy is developed, in which hard life, PM scheduled time and overhaul period of the system are revised after each service activity by adjusting PM interval and mean residual life (MRL) such that the risk of failure is not increased.

A comparative study is then carried out between the classic PM policy and developed PMP, and the improvement in availability, mean time between failures and reduction in maintenance cost is registered.

The proposed PMP takes care of the equipment degradation more efficiently than any other existing maintenance policies and is also flexible in its application as the policy can be continuously amended as per the failure profile of the equipment. Similar maintenance policies assuming lifetime distributions are available in the literature, but to ascertain that the proposed PMP is more suitable and applicable to the industries, this paper uses Kijima-based imperfect maintenance models. The proposed PMP is demonstrated through a real-time data set example.

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.

The aim of this paper is to explore the usefulness of repairable parts simple historical databases in assisting the human factors experts to identify candidate areas for applying human factors methods. Therefore, also contributing to the search for maintenance quality improvement.

The study was based on the failure history of part fleets installed on the same type of jet engines, and used mean time between failures (MTBF) and failure rates plots, the Laplace trend test, the AMSAA‐Crow‐Duane model and serial correlations.

Increasing and decreasing trends in failure rates indicated factors that cause deflection from the literature assumptions of constant failure mode and “as good as new” maintenance philosophy. Further statistical calculations revealed patterns between MTBF and frequency of maintenance, specific serial numbers (SN) vulnerability to replacement and depot maintenance tasks, correlations between MTBF and number of both installations and maintenances, and influence of the maintenance month on the maintenance‐failure hours' interval.

The literature refers to the relation between the parts reliability and the human factors in the maintenance domain. The research confirmed the literature references in data collection problems coming from human factors interferences; the patterns found were attributed to system deficiencies related to workload management, parts configuration management, supervision and manufacturing problems.

The application of this research in combination with methods such as field observations and interviews of personnel involved in the maintenance domain can uncover specific maintenance working environment weaknesses and lead to suitable remedies.

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.

The purpose of this paper is to demonstrate a tactical approach to cope with the issues related to low availability of repairable machines or systems because of their poor reliability and maintainability. It not only explores the significance of availability, but also embarks upon a step-by-step procedure to earmark a relevant replenishment plan to check the mean time between failure (MTBF) and the mean time to repair (MTTR) efficiently.

The literature review identifies the extent to which availability depends on reliability and maintainability, and highlights the diversified challenges appearing among repairable systems. Different improvement initiatives have been suggested to avoid downtime, after analyzing the failure and repair time data graphically. Relevant plots and growth curves captured the historical deviations and trends along with the time, which further helps to create more robust action plans to enrich the respective reliability and maintainability of machines. During the case study, the proposed methodology has been tested on four SPMs and successfully validated the claims after achieving around a 98 percent availability at the end.

Graphical analysis is the key to developing suitable action plans to enhance the corresponding reliability and maintainability of a machine or system. By increasing the MTBF, the reliability level can be improved and similarly quick maintenance activities can help to restore the prospect of maintainability. Both of these actions ultimately reduce the downtime or increase the associated availability exponentially.

The work revolves around the availability of SPMs. Moreover, SPMs have been divided only into series sub-systems. The testability and supportability aspects have not been considered thoroughly during the fabrication of the approach.

The work focusses on the availability of systems and proposed frameworks that helps to reduce downtime or its associated expenditure, which is generally being ignored. As a case study-based work especially on SPMs in the auto sector this paper is quite rare and will motivate affiliated engineers and practitioners to achieve future breakthroughs.