The application relates to the technical field of intelligent diagnosis of power supply systems and
optical communication cross technology, and discloses a power supply
system health diagnosis method based on adaptive multi-dimensional
ripple feature fusion, which comprises the following steps: short-time coarse sampling of a power supply
ripple signal, dynamic adjustment of sampling parameters and
window function types according to the coarse sampling result; extraction of
frequency domain features,
time domain features and time-
frequency domain features to form a multi-dimensional
feature vector; dynamic updating of a fault threshold value through Bayesian
estimation based on historical normal feature samples in a fault feature sample
library; adoption of weighted D-S evidence fusion of the fault confidence of the multi-dimensional
feature vector based on the fault threshold value, and determination of a
system state based on the fault confidence to output a diagnosis result; and hierarchical fault tracing when the
system state is an abnormal state. The application realizes adaptive sampling and
feature fusion, reduces the fault misjudgment rate, shortens the tracing time consumption, has low computing
power consumption, and is suitable for real-time
health diagnosis of a
data center optical module power supply system.