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Reliability of a product is highly dependent on the process capability index of the manufacturing process. Discusses a mathematical modelling technique for deriving the relationship between the product reliability strength and the process capability requirement to meet the product reliability strength for different types of external stress/load distributions which the product undergoes in the actual working environment. Considers four cases of external stress distributions: normal, log‐normal, expotential and Weibull. These techniques can be applied effectively in industrial production plants while selecting machines with required process capability to meet the product reliability strength demand.

Discusses the relationship between plant availability and maintenance expenditure and their limiting features. Achieving higher plant availability always necessitates a higher maintenance budget and may not be economically feasible in many cases. Through a mathematical modelling the variation of net income with respect to plant availability has been studied and the limiting availability values have been established. Expressions for optimum availability and maintenance cost have also been obtained. An illustrative example has been worked out.

When induction motors are considered, there is no specific cost model for net savings per year due to condition-based maintenance (CBM) covering various parameters such as downtime, energy, quality, etc. The purpose of this paper is to develop a cost model for the financial viability of the implementation of CBM for induction motors.

A literature review has been carried out to identify the existing failure modes of motor, available condition monitoring techniques, the usefulness of CBM and different maintenance models available. Then, a cost model considering all parameters has been proposed.

A cost model has been proposed for the maintenance of induction motors. Method for the economic evaluation of the model has also been suggested in the paper. The application of the model has been illustrated through a case study of a steel plant, which suggests that investment in the condition monitoring of induction motors increases the net profit of the organization.

The proposed model is specifically designed for induction motors. All the motors under consideration are assumed to be of the same specifications, and fault in any motor is supposed to have the same effect on quality, cost, criticality, etc., of the operation and end product.

This paper will help the maintenance manager in decision making when maintenance action has to be carried out for a given motor under CBM for the better utilization of the equipment and resources. This paper also shows how to compute ROI on CBM investment.

The paper provides a cost model for the economic evaluation of implementing CBM for induction motors which will be useful to researchers and maintenance managers in effective decision making and maintenance planning. The methodology and the cost models are the original contribution of the authors.

This paper develops a model that allows consideration of not only the total maintenance costs but also the overall probability of a system breakdown when determining the time intervals between preventive maintenance activities. Using the model, which assumes that component failures follow a Weibull distribution, managers can determine the required preventive maintenance interval to achieve a desired probability of system failure, and they can calculate the total expected costs of both breakdowns and maintenance actions. The model’s application is illustrated using the impact of four different maintenance policies. The model assures top management that the unavailable system time due to equipment breakdown will be within a specified limit.

The purpose of this paper is to propose a novel hybrid approach called as Markov System Dynamics (MSD) approach which combines the Markov approach with system dynamics (SD) simulation approach for availability modeling and to study the dynamic behavior of repairable systems.

In the proposed approach the identification of the single unit repairable system all possible states has been performed by using the Markov approach. The remaining stages of traditional Markov analysis are highly mathematically intensive. The present work proposes a hybrid approach called as MSD approach which combines the Markov approach with SD simulation approach to overcome some of the limitations of Markov process in a simple and efficient way for availability modeling and to study the dynamic behavior of this system.

The proposed framework is illustrated for a single unit repairable system. The worked out example shows the steady state point and also it gives the point, interval and steady state availabilities and also the dynamic behavior of the system. However this methodology can be extended easily for more complex multi-state maintainable systems. The results of the simulation when compared with that obtained by traditional Markov analysis clearly validate the proposed approach as an alternative approach for availability modeling of repairable systems.

In many practical situations we require to find the time at which our system reaches steady state conditions for planning maintenance activities. The proposed MSD method in this paper is capable of finding this steady state point very easily.

The proposed approach clearly indicates the time at which the system reaches its steady state and calculates the point, interval availabilities for planning maintenance activities. The different parties, i.e., engineers and machine operators, can jointly work with this model in order to understand the dynamic behavior of repairable systems.

This paper aims to explore how maintenance tasks can be planned and executed in a smarter way and, consequently, how the operations and maintenance of offshore wind power installations can be improved through modularisation.

This is a case study of one of Europe’s leading offshore wind power operators with more than 1,000 wind turbine generators in operation. By focusing on this company, in-depth insights into its operations and maintenance processes are investigated.

Lean is identified to constitute an important first step before the modularisation of maintenance tasks. The modularisation of the maintenance of offshore wind farms is identified to reduce preventive maintenance times.

The paper develops a process to identify the resources needed for maintenance before the modularisation of maintenance tasks and resources can take place. The authors also establish a foundation for the development of a software tool to support the development of the modularisation of maintenance tasks.

The present study contributes to the rather immature field of research on the operations and maintenance of offshore wind power. Furthermore, it adds to the emerging research area of service modularity.

The purpose of this paper is to optimize the preventive maintenance based on fault tree (FT)–Bayesian network (BN) reliability for sugarcane harvester machine as a fundamental machine in the sugar industry that must be operated failure-free during a given period of the harvesting process.

To determine machine reliability using the algorithm developed based on mapping FTs into BNs, the common failures of 168 machines were carefully investigated over 12 years (2007–2019). This algorithm was then used to predict the harvester reliability, estimate delays by machine downtimes and their consequences on white sugar production losses that can be reduced by optimizing the preventive maintenance scheduling.

The optimization of preventive maintenance scheduling based on estimated reliability of sugarcane harvester machines using FT–BNs can reduce white sugar production losses, the operation-stopping breakdowns and the downtime costs as a crisis that the sugar industry is facing.

Machine reliability gradually decreased by 31.08% approximately, which resulted in a working time loss of 26% in the 2018–19 harvesting season. In total, the white sugar losses were estimated as 204.17 tons for burnt canes and 114.53 tons for green canes. The losses of the 2018–19 harvesting season have been 11.85 times greater than the first harvesting season. The proposed maintenance interval for critical subsystems including the hydraulic, chopper and base cutter were obtained as 1.815, 1.12 and 1.05 h, respectively.

In this study, a new approach was used to optimize preventive maintenance to reduce delays and their implications upon costs in time, inconvenience and white sugar losses. The FT–BNs algorithm was found a useful tool that was over-fitting of failure occurrence probabilities data for sugarcane harvester machine.

To define availability importance measures in order to calculate the criticality of each component or subsystem from the availability point of view and also to demonstrate the application of such importance measures for achieving optimal resource allocation to arrive at the best possible availability.

In this study the availability importance measures of a component are defined as a partial derivative of the system availability with respect to the component availability, failure rate, and repair rate. Analyses of these measures for a crushing plant are performed and the results are presented. Furthermore, a methodology aimed at improving the availability of a system using the concept of importance measures is identified and demonstrated by use of a numerical example.

The availability importance measure of a component/subsystem is an index which shows how far an individual component contributes to the overall system availability. The research study indicates that the availability importance measures could be applied in developing a strategy for availability improvement. The subsystem/component with the largest value of importance measure has the greatest effect on the system availability.

The result of availability improvement strategy is demonstrated using only a hypothetical example.

Using availability importance measures will help managers and engineers to identify weaknesses and indicate modifications which will improve the system availability.

This paper presents the concept of availability importance measure for a component/subsystem. It also introduces some availability importance measures based on failure rate, mean time between failures (MTBF), and repair rate/mean time to repair (MTTR) of a component /subsystem. The concept of importance measures is used to prioritise the components or subsystems for the availability improvement process.

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 propose a new measure for criticality analysis of different components of a production plant, called cost‐effective importance measure (CEIM) that considers the component's performance, system structure and economic aspects.

In this work, an explorative literature study covering the concept of importance measure and criticality analysis has been carried out on contemporary literature. The literature study shows that the commonly used importance measures consider the probability of failure of a component and systems structure, and ignore the effect or severity of failures, which is an important factor in engineering decision making. It is not clear how to use the concept of importance measure in combination with cost parameter. Hence, a cost‐effective importance measure (CEIM) is defined and a case study is presented, to demonstrate the application of the proposed importance measure.

The paper indicates that CEIM useful for the analysis of production plants where reliability and cost of break down are of paramount importance.

The concept of CEIM is demonstrated using only a case study of a belt conveyor system in the underground mine of Singareni Coal Company Ltd. However, the concept of CEIM can be used in other area.

The concept of CEIM can be a handy and effective tool for scheduling of inspection, maintenance and fault diagnosis and these activities can be carried out as per the rank of the components to maximize the benefits in skilled manpower crunch. It also indicates that the upgradation of the production plant's performance can also be done by improving performance of components with relatively large CEIM values.

In this paper, the concept of importance measure is extended to include the effect of severity of failures and cost parameter in the criticality analysis.