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There is an increasing demand for research in maintenance prioritization methods, as budgets for maintenance of public buildings are unlikely to meet the maintenance needs. It is possible for government maintenance authorities to improve this situation by ensuring that the best solution is achieved in the maintenance programme through appropriate priority setting methods. A maintenance plan that is based on a rational assessment of priorities and up‐to‐date knowledge of the condition of the property stock will help to ensure the best use of available resources. Critically reviews various methods of maintenance priority setting for public buildings in the UK and Hong Kong. It also provides recommendations for maintenance managers in selecting and using the most appropriate method for their own organizations.

The purpose of this paper is to deal with the performance modeling and assessment of maintenance priorities for steam generation unit of a sugar plant.

The unit comprises of four subsystems, i.e., Bagasse elevator, Bagasse carrier, boiler and feed pump. The Chapman–Kolmogorov equations are generated on the basis of transition diagram and further solved recursively to obtain the performance modeling with the help of normalizing condition using the Markov approach.

Decision matrices are formed with the help of different combinations of failure and repair rates of all subsystems. The performance of steam generation unit is evaluated in terms of availability levels depicted in decision matrices and plots of failure rates and repair rates of various subsystems. The maintenance priorities of various subsystems of steam generation unit are decided on the basis of effect of failure and repair rates of subsystems on the availability of steam generation unit. The key finding is that the boiler subsystem is the most critical subsystem and hence should be kept on top maintenance priority for performance enhancement of the steam generation unit.

The acceptance of both performance modeling and maintenance priorities decision by the management of sugar plant will result in the enhancement of unit availability and reduction of maintenance cost.

Correct and well-planned maintenance based on modern global methods directly affects efficiency, quality, direct production costs, reliability and profitability. The selection of an optimal policy for maintenance can be a good solution for industrial units. In fact, by managing constraints such as costs, working hours and human workforce causing sudden equipment failure, production and performance can increase.

Therefore, in this research a model was presented to select the best maintenance strategy at Kaghaz Kar Kasra Co of Iran. In this study, it was tried to integrate the two techniques of goal programming and the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritize maintenance strategies. First, all factors affecting maintenance were identified, and based on the Best Worst Method (BWM) the degree of their importance was determined.

After the evaluation, only 14 criteria in the 4 dimensions of cost, added value, safety and feasibility were selected. The highest points were given to the criteria of equipment cost and depreciation, equipment and personnel performance, equipment installation time and technical feasibility, respectively. In the next stage, using the TOPSIS method the item of maintenance strategy was ranked, and the 3 strategies of preventive maintenance (PM), predictive maintenance (PDM) and corrective maintenance (CM) were chosen. Modeling was performed utilizing a goal programming approach to select the optimal maintenance strategy for 13 devices. All the technical specifications, cost limits and the device time were extracted. After the model was finished and solved the best item for each device was specified.

1. Developing a goal programming model and decision-making dashboard. 2. Identifying the criteria and factors affecting the selection of the maintenance strategy for paper production Industry

This paper describes the use of an analytical hierarchy process (AHP) in determining the rational weights of importance of pavement maintenance priority ranking factors. These weights were obtained by capturing the local people’s perception towards this vital part of the pavement management system (PMS). In this regard, different groups of individuals were asked to estimate the weight of importance in pavement maintenance of different factors for ranking pavement sections. These factors were road class, pavement condition, operating traffic, riding quality, safety condition, maintenance cost, and the overall importance of the road section to the community. The AHP method of pair‐wise comparison was employed to get the factor weights, which were compared with the weights obtained from the direct assignment method. It was concluded that the two methods were statistically similar which confirms that the results of the direct assignment method can be used safely with a sound reliability and consistency. This conclusion comes from the fact that the AHP method has a high reputation and applications, and it uses a high‐precision technique for obtaining the weights (priorities) of alternatives or items. Priority factor weights were used in developing a pavement maintenance priority ranking procedure for a road network. This procedure was validated by real case studies, and found to be logically and efficiently able to handle the ranking of a huge number of pavement sections for maintenance and repair.

The purpose of this paper is to deal with the formation of performance modeling and maintenance priorities for the water flow system (WFS) of a coal-based thermal power plant.

The system consists of five subsystems, i.e. condenser, condensate extraction pump, Low Pressure Heater, deaerator and boiler feed pump. The Chapman-Kolmogorov equations are generated on the basis of transition diagram and further solved recursively to obtain the performance modeling with the help of normalizing condition using Markov approach.

Availability matrices are formed with the help of different combinations of failures and repair rates of all subsystems. The performance of all subsystems is evaluated in terms of availability level achieved in availability matrices and plots of failure rates and repair rates of various subsystems. The maintenance priorities of various subsystems of WFS are decided on the basis of repair rate.

The adoption of both performance modeling and maintenance priorities decision by the management of thermal power plant will result in the enhancement of system availability and reduction in maintenance cost.

The purpose of this paper is to present the technique for evaluating the performance of a condensate system of a coal-based thermal power plant situated in the northern part of India. The data which used for system availability evaluation are not precise and are uncertain and, further, collected from concerned power plant history sheets and from discussion through plant personnel.

In the proposed model, traditional Markov birth-death process using a probabilistic approach is used to analyze the performance of a complex repairable condensate system of power plant up to a desired degree of accuracy. This approach has been demonstrated by breaking the condensate system into six subsystems arranged in series with two feasible states, namely, working and failed, labeled in a transition diagram and modeled as a Markov process, using Chapman–Kolmogorov equations, which are used for development of a probabilistic stochastic model for availability analysis in a more effecting manner, considering some suitable assumptions.

This study of analysis of reliability and availability can help in increasing the plant production and performance. The analysis is done with the help of availability matrices, which are developed using different combinations of failures and repair rates of all subsystems. To achieve the goal of maximum power generation, it is required to run the various subsystem of the concerned system of plant, failure free for a long duration. Therefore, the present approach may be a more powerful analysis tool to access the performance of all subsystems of a condensate system in terms of availability level achieved in availability matrices. The results of present study are found to be highly beneficial to the plant management for making maintenance decisions.

The present paper suggests a suitable technique for stochastic modeling and availability evaluation of an industrial system using Markovian approach and drawing a transition diagram to represent the operational behavior of the system. The present methodology includes the advantage of the ability to model and develop a more complex industrial system and helps in improving the performance and handling the uncertainties and possibilities of an industrial system.

This study deals with the reliability analysis of a hybrid series–parallel system consisting of two subsystems A and B with two human operators. Subsystem A has two units in active parallel while subsystem B consists of two-out-of-four units. Both units have exponential failure and repair time. The system under consideration has two states: partial failure state and complete failure state. The mathematical equations associated with the transition diagram have been formulated using regenerative point techniques. The system is analysed using Laplace transforms to solve the mathematical equations. Some important measures of reliability such as availability of system, reliability of the system, mean time to failure (MTTF), sensitivity for MTTF and cost analysis have been discussed. Some particular cases have also been derived and examined to see the practical effect of the model. The computed results are demonstrated by tables and graphs. Furthermore, the results of the designed model are beneficial for system engineers and designers, reliability and maintenance managers.

This paper considered a hybrid series–parallel system consisting of two subsystems A and B with two human operators. The performance of the system is studied using the supplementary variable technique and Laplace transforms. The various measures of reliability such as availability, reliability, mean time to system failure (MTSF), sensitivity for MTTF and cost analysis have been computed for various values of failure and repair rates. Maple 13 software has been used for computations.

In this research paper, the authors have computed various measures of reliability such as availability, reliability, MTSF, sensitivity for MTTF and cost analysis for various values of failure and repair rates and find that failure due to human operators are more responsible for successful operation of the system and also regular repair should be invoked to improve system performance.

This research paper is the original work of authors. The references are well cited based on the importance of study. Nothing has been detached from any research paper or books.

The demand of cement in India is expected to increase rapidly as the government has been giving immense boost to various housing facilities, infrastructure projects, road networks and railway corridors. One of the ways to meet this rise in the demand of cement is to increase the capacity utilization of the existing cement plants by improving their availability. The availability of a cement plant can be improved by avoiding failures and reducing maintenance time through reliability, availability and maintainability (RAM) analysis of its subsystems. The paper aims to discuss this issue.

The data related to time between failure (TBF) and time to repair (TTR) of all the critical subsystems of a cement plant were collected over a period of two years for carrying out RAM analysis. Trend test and serial correlation test were performed on TBF and TTR data to verify whether these data are independent and identically distributed or not. Afterwards, the authors use EasyFit 5.6 professional software to find best-fit distribution of TBF and TTR data and their parameters. The effectiveness of a preventive maintenance policy was evaluated by simulating the real and proposed systems.

The results of the analysis show that the raw mill and the coal mill are critical subsystems of a cement plant from a reliability point of view, whereas the kiln is a critical subsystem from an availability point of view. The analysis shows that the repair time of the cement mill should be reduced for improving the availability of the cement plant. The RAM analysis showed that the capacity of the case study company is 17 percent underutilized due to maintenance-related problems and 15 percent underutilized because of management-related problems.

The study exhibits the usage of RAM analysis in deciding preventive maintenance programs of several cement plant subsystems. Thus, it would serve as a reference for reliability and maintenance managers in deciding maintenance strategies of cement plants as well as in improving their capacity utilization.

The study exhibits the usage of RAM analysis in deciding preventive maintenance programs of several cement plant subsystems. Even more, using a simulation study, the authors show that preventive maintenance of the cement plant beyond a certain level can be disadvantageous as it leads to an increase in downtime and decrease in availability.

The purpose of this paper to present a practical and systematic approach to estimate the availability of a process plant using generalized stochastic Petri nets (GSPNs). The actual live problem at a fluid catalytic cracking unit (FCCU) of a refinery is used to demonstrate this approach.

A majority of models used for estimation of availability of a complex system are based on the assumptions that the failure of the system is associated with only a few states, and the system does not face different operating conditions, repair actions and common-cause failures. In reality, this is often not the case. Therefore, it is necessary to construct more sophisticated models without such assumptions. In this paper, an attempt has been made to model interaction of component failures, partial failures of components and common-cause failures.

The superiority of this approach over other modeling approaches such as fault tree and Markov analysis is demonstrated. The proposed GSPN is a promising tool that can be conveniently used to model and analyze any complex systems.

GSPN was used to model the reactor-regenerator section of FCCU, which is quite a large system, which shows the strength of modeling capability. The use of Petri nets (PNs) for modeling complex systems for the purpose of availability assessment is demonstrated in this paper. Sensitivity analysis was also carried out for various subsystem/components.

No similar work has been conducted for FCCU using GSPN as per literature incorporating different operating conditions and common-cause failures. The understanding and usage of PNs require a steep learning curve for the practitioners, and this paper provides an approach to estimate availability measures for the complex system.

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.