Data pre-processing methods for NPP equipment diagnostics algorithms: an overview

Abstract

The main tasks of diagnostics at nuclear power plants are detection, localization, diagnosis, and prognosis of the development of malfunctions. Analytical algorithms of varying degrees of complexity are used to solve these tasks. Many of these algorithms require pre-processed input data for high-quality and efficient operation. The pre-processing stage can help to reduce the volume of the analyzed data, generate additional informative diagnostic features, find complex dependencies and hidden patterns, discard uninformative source signals and remove noise. Finally, it can produce an improvement in detection, localization and prognosis quality. This overview briefly describes the data collected at nuclear power plants and provides methods for their preliminary processing. The pre-processing techniques are systematized according to the tasks performed. Their advantages and disadvantages are presented and the requirements for the initial raw data are considered. The references include both fundamental scientific works and applied industrial research on the methods applied. The paper also indicates the mechanisms for applying the methods of signal pre-processing in real-time. The overview of the data pre-processing methods in application to nuclear power plants is obtained, their classification and characteristics are given, and the comparative analysis of the methods is presented.