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The purpose of this paper is to study reliability, availability, and mean time to failure of a repairable robot‐safety system composed of n robots, m safety units, and a perfect switch.

Generalized expressions for system state probabilities, system availability, reliability, and mean time to failure are developed. Supplementary variable and Markov methods were used to develop these expressions.

This study clearly demonstrates that backup robots, safety units, and the repair process help to improve system availability.

This study will help maintenance engineers and reliability practitioners to become aware of the combined effect of backup robots, safety units, and the repair process on the performance of the robot‐safety system. Consequently, they will make better maintenance related decisions in organizations that make use of robots.

This paper has studied the effects of having redundant robots, safety units, and the repair facilities on the performance of a robot‐safety system with perfect mechanism to turn on a safety unit. This is one of the first attempts to study the combined effects of all these factors on a robot‐safety system.

The purpose of this paper is to investigate performance times from a quality engineering perspective for response and repair of pipes at a public utility. The objective is to scientifically determine the pipe that offers the most desirable downtime (DT) and time to repair (TTR).

Four types of water supply pipes – concrete cylinder (CC), cast iron (CI), ductile iron (DI), and polyvinyl chloride (PVC) – in prevalent use at the City and County of Honolulu Board of Water Supply were analyzed to determine the pipe type that is most consistently repaired to desired performance specifications. Data for mean downtime (MDT) and mean time to repair (MTTR) were used to evaluate the stability and capability of the repair processes for each pipe type. The analysis was completed through the use of control charts, operating characteristic (OC) curves, and process capability indices.

The results of the analysis indicated that CI pipes were the worst material in terms of DT and TTR. The control charts for MDT for all pipe types, and the MTTR for CI and CC pipes, were found to be out of statistical control, but the control charts for the MTTR of DI and PVC pipes were discovered to be in control. According to the OC curves, in which the hypothesis stated that the average MDT or MTTR was between the specification limits, there was a high tendency in all pipe types to accept the hypothesis when it was true. However, the probability of type I errors was high from operational standards at the USL level. Process capability analyses found that only CC pipes were able to meet performance design specifications; however, repair times are extremely large for CC pipes. Overall, it is recommended that CI pipes be replaced when the opportunity arises.

This investigation serves to address a major query in asset management at the public utility, that of which pipes should be selected during design and procurement from a maintenance perspective. In addition, the study helps to understand the trend of DT and TTR for the various pipes.

Quality water supply is of paramount social importance in modern cities.

A quality engineering approach to asset management for pipe systems at public utilities that serves to add a new dimension to asset performance analysis is adopted.

The purpose of this paper is to provide results for a complete maintainability analysis utilizing data sets from a production system in a shaving blades division of a large high-tech razor manufacturer. Through the illustrated case study, the authors demonstrate how to spot improvement points for enhancing availability by carrying out an equipment effectiveness analysis.

Descriptive statistics of the repair data and the best fitness index parameters were computed. Repair data were collected from departmental logs, and a preliminary screening analysis was conducted to validate their usefulness for the indicated period of 11 months. Next, the maintainability and repair rate modes for all the machines of the production system were calculated. Maintainability and availability estimations for different time periods that took in account the overall system were obtained by trying out and selecting an optimum statistical model after considering of several popular distributions.

Out of the five considered machines in the system, two particular units received about half of the repairs (M2 and M3). The time to repair follows a loglogistic distribution and subsequently the mean time to repair is estimated at 25 minutes at the machine level. Repair time performance is approximated at 55 minutes if the availability of the system is to attain a 90 percent maintainability.

This study is anticipated to serve as an illuminating effort in conducting a complete maintainability analysis in the much advertised field of shavers, for which on the contrary so little has been published on operational availability and equipment effectiveness. The case study augments the available pool of sources where executing maintainability studies is highlighted usually under the direction of combined total quality management and total productive maintenance programs.

This paper presents reliability and availability analyses of a robot‐safety system having one robot and n‐redundant safety units with common‐cause failures. The system failure rates and the partially failed system repair rates are assumed constant, and the failed system repair time is assumed arbitrarily distributed. Markov and the supplementary variable methods were used to perform mathematical analysis of this model. Generalized expressions for state probabilities, system availabilities, reliability, mean time to failure, and variance of time to failure are developed. Some plots of these expressions are shown.

Wind power is an important source of renewable energy and accounts for significant portions in supplying electricity in many countries and locations. The purpose of this paper is to develop a method for wind power system reliability assessment and condition-based maintenance (CBM) optimization considering both turbine and wind uncertainty. Existing studies on wind power system reliability mostly considered wind uncertainty only and did not account for turbine condition prediction.

Wind power system reliability can be defined as the probability that the generated power meets the demand, which is affected by both wind uncertainty and wind turbine failures. In this paper, a method is developed for wind power system reliability modeling considering wind uncertainty, as well as wind turbine condition through health condition prediction. All wind turbine components are considered. Optimization is performed for maximizing availability or minimizing cost. Optimization is also conducted for minor repair activities to find the optimal number of joint repairs.

The wind turbine condition uncertainty and its prediction are important for wind power system reliability assessment, as well as wind speed uncertainty. Optimal CBM policies can be achieved for optimizing turbine availability or maintenance cost. Optimal preventive maintenance policies can also be achieved for scheduling minor repair activities.

This paper considers uncertainty in both wind speed and turbine conditions and incorporates turbine condition prediction in reliability analysis and CBM optimization. Optimization for minor repair activities is studied to find the optimal number of joint repairs, which was not investigated before. All wind turbine components are considered, and data from the field as well as reported studies are used.

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 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.

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.

The purpose of this paper is to deliberate the system reliability of a system in combination of three subsystems in a series configuration in which all three subsystems function under a k-out-of-n: G operational scheme. Based on computed results, it has been demonstrated that copula repair is better than general repair for system better performance. The supplementary variable approach with implications of copula distribution has been employed for assessing the system performance.

Probabilistic assessment of complex system consisting three subsystems, multi-failure threats and copula repair approach is used in this study. Abbas Jubrin Bin, V.V. Singh, D.K. Rawal, in this research paper, have analyzed a system consisting of three subsystems in a series configuration in which all three subsystems function under a k-out-of-n: G operational scheme. The supplementary variable approach with implications of copula distribution has been employed for assessing the system performance. Based on computed results, it has been demonstrated that copula repair is better than general repair for system better performance.

In this analysis, four different cases of availability are analysed for Gumbel–Hougaard family copula and also four cases for general repair with similar failure rates are studied. The authors found that when failure rates increase, the system availability decreases, and when the system follows copula repair distribution, the system availability is better than general repair.

This research may be implemented in various industrial systems where the subsystems are configured under k-out-of-n: G working policy. It is also advisable that copula repair is highly recommended for best performances from the system. On the basis of mean time to system failure (MTSF) computations, the failure rate which affects system failure more needs to be controlled by monitoring, servicing and replacing stratagem.

This research work has great implications in various industrial systems like power plant systems, nuclear power plant, electricity distributions system, etc. where the k-out-of-n-type of system operation scheme is validated for system operations with the multi-repair.

This work is a new work by authors. In the previously available technical analysis of the system, the researchers have analyzed the repairable system either supplementary variable approach, supplementary variable and system which have two subsystems in a series configuration. This research work analyzed a system with three subsystems with a multi-repair approach and supplementary variables.

This paper deals with the performance optimization and sensitivity analysis for crystallization system of a sugar plant.

Crystallization system comprises of five subsystems, namely crystallizer, centrifugal pump and sugar grader. The Chapman–Kolmogorov differential equations are derived from the transition diagram of the crystallization system using mnemonic rule. These equations are solved to compute reliability and steady state availability by putting the appropriate combinations of failure and repair rates using normalizing and initial boundary conditions. The performance optimization is carried out by varying number of generations, population size, crossover and mutation probabilities. Finally, sensitivity analysis is performed to analyze the effect of change in failure rates of each subsystem on availability, mean time to failure (MTBF) and mean time to repair (MTTR).

The highest performance observed is 96.95% at crossover probability of 0.3 and sugar grader subsystem comes out to be the most critical and sensitive subsystem.

The findings of the paper highlights the optimum value of performance level at failure and repair rates for subsystems and also helps identify the most sensitive subsystem. These findings are highly beneficial for the maintenance personnel of the plant to plan the maintenance strategies accordingly.