Predictive maintenance within combined heat and power plants based on a novel virtual sample generation method

Abstract

Combined heat and power plants are important assets in the current energy infrastructure, as they play an essential role in ensuring the security of energy supply. Therefore, they must have low downtime. Currently, preventive maintenance is the most popular maintenance strategy for combined heat and power plants. The preventive maintenance includes performing regular, scheduled maintenance checks and repairs, whether they are needed or not. The preventive maintenance is effective to a certain degree and reduces the downtime. However, there is a need for it to be upgraded to predictive maintenance. The current preventive maintenance strategy creates a barrier for combined heat and power plants to adopt a predictive maintenance framework as limited samples of faulty data will be available. To overcome this issue, this paper proposes a novel virtual sample generation method. The method creates a set of lifetime ratios based on the actual faults, Random Walks, and particle swarm optimisation faults to generate virtual samples. The method is implemented on two case studies. The first case study is used to evaluate the method on a benchmark example with publicly available data. The second case study is a set of feedwater pumps in the Danish Power Plant, Studstrupværket, run and operated by Ørsted. This case study is used for field testing and evaluation. The findings indicate that the method significantly improves the accuracy and robustness of the remaining-useful-lifetime model. The approach furthermore allows for using expert knowledge, which could be attractive for other energy applications.