Modelling the effects of maintenance on the degradation of a water-feeding turbo-pump of a nuclear power plant

Abstract

This work addresses the modelling of the effects of maintenance on the degradation of an electric power plant component. This is done within a modelling framework previously proposed by the authors, of which the distinguishing feature is the characterization of the component living conditions by influencing factors (IFs), i.e. conditioning aspects of the component life that influence its degradation.The original fuzzy logic-based modelling framework includes maintenance as an IF; this requires one to jointly model its effects on the component degradation together with those of the other influencing factors. This may not come natural to the experts who are requested to provide the if-then linguistic rules at the basis of the fuzzy model linking the IFs with the component degradation state. An alternative modelling approach is proposed in this work, which does not consider maintenance as an IF that directly impacts on the degradation but as an external action that affects the state of the other IFs. By way of an example regarding the propagation of a crack in a water-feeding turbo-pump of a nuclear power plant, the approach is shown to properly model the maintenance actions based on information that can be more easily elicited from experts.

Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

“…Having forecasted the temperatures for the operation time horizon of 15 years, one can obtain the values of the temperature-dependent factors for system components by comparing the forecasted daily temperatures against a set of pre-determined thresholds. This study assumes the range specified by Equation (24).…”

“…For example, qualitative information is used to directly modify the Mean Time to Failure (MTTF) of components for electrical production plant components [24,25] and of mechanical equipment units operating in an Arctic offshore oil processing train [26]. These works generalize the practical approach proposed in [27] by putting emphasis on the treatment of uncertainty and imprecision related to the information sources used to estimate the parameters of the models, but they do not account for the accumulated effects of the covariates on the equipment failure behaviour.…”

Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components

“…For example, operators typically assign a qualitative tag to equipment health during periodic inspections. Multistate modeling has also been adopted to describe the evolution of degradation in membranes of pumps operated in nuclear power plants [7], in turbine nozzles for the oil and gas industry [8], in components of the electrical industry [9,10], liners of marine diesel engine cylinders [2,11], and piping of nuclear power plants [12]. Finally, a strong advantage of multistate degradation models is that they exploit sound, well-established mathematical techniques for their quantification, such as the Markov and semi-Markov models.…”

Genetic algorithms for condition-based maintenance optimization under uncertainty

“…Now, the aim of the present work is to propose an extension of Multi-Objective Genetic Algorithms (MOGA [14], [27], [35], [38], [43]) to tackle the maintenance optimization issue when the epistemic uncertainty in the degradation model is represented in the probability theory framework. Namely, the parameters of the stochastic model of the degradation mechanisms are supposed to be Maximum Likelihood (ML)-estimated and the uncertainties in these estimations are represented by probability distributions [4], [25], [48]. A double-loop Monte-Carlo approach [61] is used to propagate the uncertainties from the model parameters onto the considered performance indicators (i.e., the objective functions of the optimization, which represent fitness values of the solutions), which turn out to be probability distributions.…”