The invention relates to the technical field of
damage detection of oil and gas transportation pipelines, in particular to a marine pipeline damage degree intelligent identification method based on multi-source feature deep fusion. The method comprises the following steps: S1, acquiring damage data of a damaged pipeline; s2, performing
noise reduction on the damage data by adopting an improved
wavelet threshold
noise reduction method; s3, performing normalization
processing on the data after
noise reduction; s4, inputting the normalized data into a preset multi-feature deep fusion
white box model for training; and S5, calculating the accuracy to reflect the accuracy of model identification. The method overcomes the defects of a hard
threshold function and a soft
threshold function, flexibly switches between the hard
threshold function and the soft threshold function by adjusting the factor alpha, reduces discreteness, avoids the problem of constancy, is high in adaptability, can be automatically adjusted according to different
signal characteristics, effectively removes the noise of experimental acquisition data, improves the
signal quality, and greatly improves the identification accuracy of the damage degree.