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The purpose of this paper is to make a performance model of a shoe upper manufacturing unit of a shoe manufacturing industry by computing both the availabilities, i.e. time dependent system availability (TDSA) and the long‐term availability.

The present work is carried out by developing performance model based on Markov birth‐death process. The unit consists of four subsystems. The first order governing differential equations are derived using the mnemonic rule and further solved by adaptive step‐size control Runge‐Kutta method to calculate the TDSA, while the long‐term availability is calculated using normalizing condition, initial boundary conditions and recursive method. Both the availabilities are considered for system's performance criterion.

The subsystem A, i.e. sewing machine is the most critical from maintenance point of view, which has more impact on the system's performance as compare to other subsystems. The repair priorities of other subsystems have also been proposed.

These methods can also be used to find out the performance of other manufacturing industries.

The results of the present work are very useful for finding the critical subsystem and its effect on the system performance in terms of availability. Further, based on findings the maintenance priorities of various subsystems can be decided.

Degraded failures and sudden critical failures are quite prevalent in industries. Degradation processes commonly belong to Weibull family and critical failures are found to follow exponential distribution. Therefore, it becomes important to carry out reliability and availability analysis of such systems. From the reported literature, it is learnt that models are available for the situations where the degraded failures as well as critical failures follow exponential distribution. The purpose of this paper is to present models suitable for reliability and availability analysis of systems where the degradation process follows Weibull distribution and critical failures follow exponential distribution.

The research uses Semi-Markov modeling using the approach of method of stages which is suitable when the failure processes follow Weibull distribution. The paper considers various states of the system and uses state transition diagram to present the transition of the system among good state, degraded state and failed state. Method of stages is used to convert the semi-Markov model to Markov model. The number of stages calculated in Method of stages is usually not an integer value which needs to be round off. Method of stages thus suffers from the rounding off error. A unique approach is proposed to arrive at failure rates to reduce the error in method of stages. Periodic inspection and repairs of systems are commonly followed in industries to take care of system degradation. This paper presents models to carry out reliability and availability analysis of the systems including the case where degraded failures can be arrested by appropriate inspection and repair.

The proposed method for estimating the degraded failure rate can be used to reduce the error in method of stages. The models and the methodology are suitable for reliability and availability analysis of systems involving degradation which is very common in systems involving moving parts. These models are very suitable in accurately estimating the system reliability and availability which is very important in industry. The models conveniently cover the cases of degraded systems for which the model proposed by Hokstad and Frovig is not suitable.

The models developed consider the systems where the repair phenomenon follows exponential and the failure mechanism follows Weibull with shape parameter greater than 1.

These models can be suitably used to deal with reliability and availability analysis of systems where the degradation process is non-exponential. Thus, the models can be practically used to meet the industrial requirement of accurately estimating the reliability and availability of degradable systems.

A unique approach is presented in this paper for estimating degraded failure rate in the method of stages which reduces the rounding error. The models presented for reliability and availability analyses can deal with degradable systems where the degradation process follows Weibull distribution, which is not possible with the model presented by Hokstad and Frovig.

The purpose of this paper is to prepare a methodology to evaluate availability analysis of distillery plant using Petri nets (PN). The effect of various failures, repair rate and availability of repair facilities on the system has been studied.

The PN model of a system is developed to study the dynamic behavior of the system under various working conditions. Availability analysis of the plant is presented taking into consideration of failure and repair rates of their subsystems and has been carried out using Monte Carlo simulation.

The bottle washing machine is the most critical machine from a maintenance point of view which has more compact on the system performance as compared to other machines. The availability analysis of distillery plant helps the management to adopt a suitable maintenance policy to improve upon plant production and plant availability.

The application of this proposed PN-based approach is very useful in finding the most critical subsystem and its effect on the performance of the system in terms of availability and plant production. Further, the advantage of the PN approach is that dynamic behavior of the system under consideration is modeled with graphic representation of the system and given distribution can be associated with the subsystems for analysis. The superiority of this approach over others, such as network, fault tree and Markov analysis, is outlined in the paper.

The purpose of this paper is to optimize the performance for complex repairable system of paint manufacturing unit using a new hybrid bacterial foraging and particle swarm optimization (BFO-PSO) evolutionary algorithm. For this, a performance model is developed with an objective to analyze the system availability.

In this paper, a Markov process-based performance model is put forward for system availability estimation. The differential equations associated with the performance model are developed assuming that the failure and repair rate parameters of each sub-system are constant and follow the exponential distribution. The long-run availability expression for the system has been derived using normalizing condition. This mathematical framework is utilized for developing an optimization model in MATLAB 15 and solved through BFO-PSO and basic particle swarm optimization (PSO) evolutionary algorithms coded in the light of applicability. In this analysis, the optimal input parameters are determined for better system performance.

In the present study, the sensitivity analysis for various sub-systems is carried out in a more consistent manner in terms of the effect on system availability. The optimal failure and repair rate parameters are obtained by solving the performance optimization model through the proposed hybrid BFO-PSO algorithm and hence improved system availability. Further, the results obtained through the proposed evolutionary algorithm are compared with the PSO findings in order to verify the solution. It can be clearly observed from the obtained results that the hybrid BFO-PSO algorithm modifies the solution more precisely and consistently.

There is no limitation for implementation of proposed methodology in complex systems, and it can, therefore, be used to analyze the behavior of the other repairable systems in higher sensitivity zone.

The performance model of the paint manufacturing system is formulated by utilizing the available uncertain data of the used manufacturing unit. Using these data information, which affects the performance of the system are parameterized in the input failure and repair rate parameters for each sub-system. Further, these parameters are varied to find the sensitivity of a sub-system for system availability among the various sub-systems in order to predict the repair priorities for different sub-systems. The findings of the present study show their correspondence with the system experience and highlight the various availability measures for the system analyst in maintenance planning.

The purpose of this paper is to propose a method to compute RAMD indices to measure and improve the performance of skim milk powder production system of a dairy plant under real working conditions.

The present work is carried out by developing performance model based on Markov birth-death process. The skim milk powder production system consists of six units. The first order governing differential equations are derived using the mnemonic rule and further solved to calculate RAMD indices i.e. reliability, availability, maintainability, dependability, MTBF, MTTR and dependability ratio for each subsystem of the system.

The subsystem SS1 comprising of chiller and cream separator is the most critical from maintenance point of view, as the reliability, availability, maintainability, dependability, MTBF and dependability ratio indices are low as compared to those of other subsystems of skim milk powder production system of the dairy plant.

The RAMD indices of the present work is very useful for finding the critical subsystem and its effect on the performance of the system working under real working conditions. Further, based on findings the maintenance priorities for various subsystems can be decided.

The purpose of this research is to calculate and enhance the cheese cheddar manufacturing plant efficiency under actual workplace conditions by measuring reliability, availability and maintainability (RAM) indices. The authors highlight how RAM analysis is important in determining periodic maintenance and in scheduling and managing the appropriate maintenance policy.

The current work is conducted using statistical approaches to evaluate failure and repair statistics. The RAM estimation was calculated on the basis of quantitative data obtained over a span of 32 months. Descriptive statistics, Pareto analysis, as well as the presumption of independence were ensured through trend and serial correlation tests. In addition, the reliability and maintainability of the cheddar cheese processing plant and its machines were calculated at various mission periods.

The primary goal of the implementation approach is to understand the fault patterns and the accurate quantitative assessment of the reliability and maintainability of the cheddar production plant. The findings revealed the essential aspects of the line, which need improvement by an appropriate maintenance program.

This study is intended to serve to highlight the RAM assessment and its impact on the performance of the real-time system. The benefit of the technique is the continual control of the manufacturing process by means of acceptable indexes, whose use corresponds to a continuous improvement process.

Lacquers based on polystyrene and polyvinyl chloride resins were prepared by blending the two resins in various proportions. Their properties were evaluated and compared with those of a commercial leather lacquer based on polyvinyl chloride resin of organosol type. It was found that some of the blends proved to be comparable and even superior, in some respect, to the commercial sample.

The purpose of this paper is to identify the criticality of various sub-systems through the behavioral study of a multi-state repairable system with hot redundancy. The availability of the system is optimized to evaluate the optimum combinations of failure and repair rate parameters for various sub-systems.

The behavioral study of the system is conducted through the stochastic model under probabilistic approach, i.e., Markov process. The first-order differential equations associated with the stochastic model are derived with the use of mnemonic rule assuming that the failure and repair rate parameters of all the sub-systems are constant and exponentially distributed. These differential equations are further solved recursively using the normalizing condition to obtain the long-run availability of the system. A particle swarm optimization (PSO) algorithm for evaluating the optimum availability of the system and supporting computational results are presented.

The maintenance priorities for various sub-systems can easily be set up, as it is clearly identified in the behavioral analysis that the sub-system (A) is the most critical component which highly influences the system availability as compared to other sub-systems. The PSO technique modifies input failure and repair rate parameters for each sub-system and evaluates the optimum availability of the system.

A bottom case manufacturing system is under the evaluation, which is the main component of front shock absorber in two-wheelers. The input failure and repair rate parameters were parameterized from the information provided by the plant personnel. The finding of the paper provides the various availability measures and shows the grate congruence with the system behavior.

Describes some of the successful innovations to be seen at Unishoe (Universal Shoe Machine Company Limited), such as a fully automated sole preparation line; a stress reliever lamp for removing creases from shoe uppers; machines for fitting metal shanks to the “shankboards” for stiffening insoles; and machines for conditioning and lasting uppers into shape.

The present paper investigated a skim milk powder production plant with genuine human mistake for analyzing its performance in terms of its reliability, availability and maintainability (RAM) indices along with mean time between failure (MTBF) and expected number of failure (ENOF).

In the proposed work, the generalized fuzzy lambda–tau methodology has been used to carry out the analysis of the repairable structure using the improved arithmetic operations for generalized fuzzy numbers by considering the degree of confidence levels.

RAM indices along with MTBF and ENOF are obtained to increase the quality of skim milk powder manufacturing structures of a dairy plant with genuine human-mistake working conditions.

In the present paper, a mathematical model for a complex industrial system based on fuzzy has been developed. Finally, the results are more realistic and comprehensive for the decision-maker for farther application.