This application relates to the field of fault prediction technology, specifically to a method and
system for monitoring and early warning of fault risks in
nuclear power plant equipment. Specifically, it includes: constructing a first fluctuation feature value based on the randomness of the peak
occurrence time and the fluctuation characteristics of the
signal sharpness in the vibration
signal of a
planetary gearbox bearing in a
nuclear power plant; constructing a second fluctuation feature value by analyzing the distribution of
sideband features in the envelope spectrum of the
bearing vibration signal in each sampling period and comparing it with the
center frequency of the previous sampling period; appropriately adjusting the filter length based on both factors, and combining this with a filtering
algorithm to extract fault feature signals from the
bearing vibration signal in each sampling period, thereby performing fault detection of the
planetary gearbox within each sampling period. This solves the problem of inappropriate filter length values when filtering the
bearing vibration signal of a
nuclear power plant planetary gearbox, improves the extraction accuracy of fault feature signals, and contributes to the accuracy of subsequent fault detection.