Busy Period Of Server Research Articles

Sensitivity Analysis of Edible Oil Refinery Industry using Machine Learning

This research paper discusses the use of machine learning for sensitivity analysis in the edible oil refinery industry. The edible oil refinery consists of four units, and subsystem A (Cleaning) contains parallel subcomponents that enable it to function in reduced capacity when some of its components fail. As a result, the system operates in reduced capacity and fails entirely when unit A fails. There is single repairman who is available for 24x7. The failure and repair time distributions are distinct and constant for each unit. Subunits B (Shelling), D (Crushing/Ribbing), and E (Expeller Pressing) are connected in series, so, if any of them malfunctions, the entire system fail. System parameters' behavior for various repair and failure rates is discussed graphically and in tabular form. Comparison of the Performance of system parameters is carried out using linear Classifier and Logistic Regression and graphs are drawn. From the comparative analysis of models, it is concluded that the linear classifier is better than logistic regression for MTSF, Busy Period of server and Availability.

Mathematical modeling and behavioral analysis of a washing unit in paper mill

The objective of this paper is to study behavioral analysis of a washing unit in paper industry for system parameters using RPGT and taking Particular cases. Observe the behaviour of system having each kind of failures partial in addition to complete failure and check the feasibility of repair of failed framework on partial and complete failure. The state diagram of the system is drawn taking into account all probability considerations, failures and repair rates of units’ system parameter equations are written using RPGT. Failure/repair rates at subsystems level are considered to be constant for modeling study state parameters (e.g. MTSF, availability, busy periods of server’s and Expected Fractional Number of server) which are important for plant managements. Particular cases and behavioral analysis w.r.t. various failures/repair rates are carried out which are followed by tables, graphs and a conclusion. Results represented below are practical, behavioral analysis for the system parameters i.e. sensitivity analysis needs to be done further required being analyzed more improvement in the system.

Comparison of reliability characteristics of two semi-Markov repairable systems under degradation and abnormal environment

This article launches two stochastic models of a single-unit system with the notion of degradation and abnormal environment. In both models, system becomes degraded after repair while preventive maintenance of the original or degraded unit is perfect. There is a single server in both models; in the second model no effect of abnormal environment is considered and he only performs repair and maintenance activity whereas in first model he does all these activities in abnormal environment also. The system remains still working under abnormal environment such as mild/harsh, maximum/minimum level demand, etc. Some important reliability characteristics, transition probabilities, mean sojourn time, availability, mean time to system failure and busy period of server are derived via semi-Markov processes and regenerative point technique. The mathematical expressions and numerical results for differences between reliability characteristics have been also provided for three cases: (1) when random variables have Weibull distribution; (2) when random variables have exponential distribution as a particular case of Weibull distribution under shape parameter equal to one; and (3) when random variables have Rayleigh distribution as a particular case of Weibull distribution under shape parameter equal to two.

A Cold-Standby System with Server Failure and Delayed Treatment

The paper stochastically investigates a two unit cold-standby system with a server subject to failure and getting delayed treatment thereafter. Semi-Markov process is used to develop the system model. The model is analyzed at different regeneration points using regenerative-point technique. The steady-state expressions are derived for various system performance measures such as mean time to failure, availability, busy period of server, expected number of treatments, profit etc. Finally, numerical examples are given to discuss the effect of various parameters on system performance measures.