Selective Maintenance Strategy For Generating Set

As the hydroelectric set system to develop in the direction of large-scale, complicate and precise, the system present various kinds of performance or several kinds of failure modes. The hydroelectric set maintenance method executed as a fixed period will bring insufficient maintenance and superfluous maintenance. Therefore, traditional maintenance is not able to meet the needs of modern enterprise in nowadays fierce competition. Condition-based maintenance is a kind of equipment maintenance mode. According to status monitoring and fault diagnosis, maintenance should be scheduled properly so as to avoid equipment damage occurs suddenly, causing enormous losses.Considerable benefits have been gained from using Markov decision process to use condition-based maintenance policies for the hydroelectric generator sets. A key part of the method is using a Markov process to model the deterioration of condition. However, the Markov model assumes the transition time between any states is distributed exponentially. This fact seriously restricts the application of the Markov chain model to real-world problems. Therefore, this paper investigates processes with arbitrarily distributed sojourn times is to use a semi-Markov process model. The main advantage of a semi-Markov model is that it allows non-exponential distributions for transitions between states and generalizes several kinds of stochastic process. Since in many real cases, the lifetime and repair times are not exponential, this is very important. For example, the breakage of the principal axis’ bearing can result the failure of the whole generator, Weibull distribution can model the deterioration of the generator condition.This dissertation presents the research about tracks degradation trend and an optimal maintenance strategy of the hydroelectric set. The major content in this paper can be separated into three parts listed below:(1) The classical Markov process is mainly used to model the deterioration trend in condition based maintenance, which assumes the transition time between any states is distributed exponentially. In order to give more precise explanation of the deterioration of the hydroelectric set and prediction of the reliability change trend in the future, this paper utilizing Semi-Markov process to construct the component of the hydropower station deterioration model. Many cases have been used to prove the accuracy and feasibility of the mentioned model.(2) In the engineering practice, the cost involved in the maintenance usually assumed as constant. This is not corresponded to engineering practice. For this sake, an extended dynamic control-limit policy is proposed from the constant control-limit policy for hydroelectric system, and the proposed control-limit is proportional to downtime-cost for conditional based maintenance. The aim of the proposed policy is to reduce the maintenance cost rate by reducing the possibilities of performing high downtime periods and increase reliability by increasing the possibilities of performing low downtime periods. Additionally, a case study highlights the effectives of the proposed policy compared to the constant control-limit policy.(3) Investigation of the maintenance policy for hydroelectric components during successive mission breaks. A novel selective maintenance model for hydroelectric system is proposed. In this model, the component inspection data and imperfect maintenance factor are taken into account. The particle swarm algorithm is employed to solve the optimization problem. Numerical studies show that the hydroelectric systems can achieve a higher mission completion rate by using the proposed method.