The data forecasting of plant equipment plays an important role in assurance of the safe and reliable operation of the plant equipment. Thus, it is necessary to improve the accuracy of data forecasting of the equipment. A new two-factor fuzzy time series algorithm is proposed to forecast the data of the plant equipment. This method not only overcomes the limitations of one factor fuzzy time series algorithm, but also overcomes the drawbacks of traditional two-factor fuzzy time series algorithm. The collected data is used in the power plant to conduct experiments, where the metrics is Mean Absolute Percentage Error (MAPE). The results show that this method is superior to the existing two-factor fuzzy time series algorithms, and yields good results in the equipment prediction.
Under the complex condition of nuclear power plant, all kinds of influence factors may cause distortion of on-line monitoring data. It is essential that on-line monitoring data should be de-noised in order to ensure the accuracy of diagnosis. Based on the research of wavelet analysis and threshold de-noising, a new threshold denoising method based on Mallat transform is proposed. This method adopts factor weighing method for threshold quantization. Through the specific case of nuclear power plant, it is verified that the algorithm is of validity and superiority.
针对核电站预防维修大纲的编制给出了具体的流程,并结合目前国内外的维修优化方法,提出了大纲优化的策略和方法,包括优化原则和RCM分析方法应用等内容。
This paper introduces the background for the ongoing research on accelerated ageing management (AAM) technology for important and susceptible components in Daya Bay nuclear power plant (NPP) and Lingao NPP. The technical procedure, the work architecture and the interfaces with existing operation and maintenance system in NPPs are also detailed. Finally, some representative examples of the use of AAM to solve practical operational issues are presented, before concluding on propositions for the future development of this technology. Keyword: accelerated ageing; accelerated ageing management; important and susceptible components; maintenance strategies
