Stochastic modelling and availability analysis of a critical engineering system

Abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Optimization tools applied to physical asset maintenance management: state of the art

“…The Markov chain can be seen as a decision support tool for managers concerning the organization of equipment maintenance [40]. It also takes advantage of the traditional probabilistic birth-death approach of Markovian models by using a probabilistic approach to analyze the performance of a system (power plant); performing reliability and availability studies on the plant [41].…”

Reliability Availability Maintainability Analysis in Forming Industry

“…The mathematical equations and decision matrix of failure rate, repair rate variations are measured by using the MATLAB programming tool. Sorabh Gupta [2] detailed discussed about the performance analysis of the water condensate system of the coal-based thermal power plant in northern India. According to this research they are analysed one of the subsystem (water condensate system) in the thermal power plant and suggest the suitable technique for stochastic modelling and availability evaluation of the given sub system of the thermal power plant by using the traditional Markov model approach and Chapman -Kolmogorov equations are developed by the transition diagram of the water condensate system in thermal power plant.…”

Investigation of various maintenance parameters for auxiliaries in a coal‐handling plant aided by a Visual Basic 6.0–based scheduled program

“…[8][9][10][11][12][13][14][15] Most optimization studies are observed to be based on mathematical modeling, which are time consuming and complicated, and well suited for large process plants. [15][16][17][18][19][20][21][22][23][24][25] Therefore, this software has been developed to address such issues for CHP department in thermal power station. It has a major advantage over existing models due to its applicability to large and small-scale process plants.…”

“…Monetary dependence leads to either cost savings or additional costs due to component group maintenance. Gupta reported that to achieve the goal of maximum power generation from coal‐based power plant it is required to run the various subsystem of the concerned system of plant, failure‐free for a long duration. Özgür‐Ünlüakın et al formulated corrective maintenance methodology through dynamic Bayesian network to address regenerative air heating systems in thermal energy plants with many interacting parts.…”

“…Several works exist on maintenance optimization for fault diagnosis in various process plants in the literature . Most optimization studies are observed to be based on mathematical modeling, which are time consuming and complicated, and well suited for large process plants . Therefore, this software has been developed to address such issues for CHP department in thermal power station.…”