Modeling multi-state system reliability analysis in a power station under fatal and nonfatal shocks: a simulation approach

Abstract

PurposeIn some environments, the failure rate of a system depends not only on time but also on the system condition, such as vibrational level, efficiency and the number of random shocks, each of which causes failure. In this situation, systems can keep working, though they fail gradually. So, the purpose of this paper is modeling multi-state system reliability analysis in capacitor bank under fatal and nonfatal shocks by a simulation approach.Design/methodology/approachIn some situations, there may be several levels of failure where the system performance diminishes gradually. However, if the level of failure is beyond a certain threshold, the system may stop working. Transition from one faulty stage to the next can lead the system to more rapid degradation. Thus, in failure analysis, the authors need to consider the transition rate from these stages in order to model the failure process.FindingsThis study aims to perform multi-state system reliability analysis in energy storage facilities of SAIPA Corporation. This is performed to extract a predictive model for failure behavior as well as to analyze the effect of shocks on deterioration. The results indicate that the reliability of the system improved by 6%.Originality/valueThe results of this study can provide more confidence for critical system designers who are engaged on the proper system performance beyond economic design.

Untitled

“…A proper number of points on each surface are selected in each area and an average of their CFPs (ACFP) is calculated. The results can be seen in tables (9,10,11). The last column of the Tables shows the percentage of difference of the ACFPs (as a representative of reliability and performance) between SDS and FOVS.…”

“…Indeed, reliability modeling of the former is far more difficult than the latter and is often accomplished by computer simulation techniques. Interested readers could refer to [11] for deterministic changes, [4] for stochastic changes and [18] for ranking defects.…”

“…The reason for this is the existence of different random variables and the complexity of systems analysis by analytical methods. For example, we can refer to [1,11], and [13], whose methods have been cited by many researchers.…”

Decision-making framework for supplier selection using an integrated MCDM approach in a lean-agile-resilient-green environment: evidence from Indian automotive sector

“…Today, it is required to manage organizational resources with extent performance marks to converse existing resources into value-added streams (Keramati et al, 2018;Bag et al, 2021b). Furthermore, the conversion of the resources can be effectual maintained by the identification of critical factors, strategic decisions based on knowledge management (Apornak and Keramati, 2017;Pourhassan et al, 2021). Moreover, strategic decisions can be made by the integration of Decision Support Framework (DSF) and that may lead in changing priorities of the customers toward organization (Apornak, 2019;Kumar et al, 2020).…”

“…, 2021b). Furthermore, the conversion of the resources can be effectual maintained by the identification of critical factors, strategic decisions based on knowledge management (Apornak and Keramati, 2017; Pourhassan et al. , 2021).…”