  Large scale data storage is susceptible to failure. As disks are damaged and
replaced, traditional machine learning models, which rely on historical data to
make predictions, struggle to accurately predict disk failures. This paper
presents a novel method for predicting disk failures by leveraging multi-layer
domain adaptive learning techniques. First, disk data with numerous faults is
selected as the source domain, and disk data with fewer faults is selected as
the target domain. A training of the feature extraction network is performed
with the selected origin and destination domains. The contrast between the two
domains facilitates the transfer of diagnostic knowledge from the domain of
source and target. According to the experimental findings, it has been
demonstrated that the proposed technique can generate a reliable prediction
model and improve the ability to predict failures on disk data with few failure
samples.
