The invention discloses a
submarine pipeline migration fault diagnosis method,
system and equipment based on a bidirectional
diffusion bridge mechanism, belongs to the technical field of energy infrastructure fault detection and intelligent diagnosis, and aims to solve the problem of performance degradation caused by field distribution offset when an existing model processes a variable-working-condition
submarine oil pipeline monitoring task. And the
false alarm and missing alarm risks in the fault detection process are effectively reduced. According to the technical key points,
cross entropy loss and boundary expansion loss are utilized to
train a fault diagnosis model based on source domain data; a forward
diffusion bridge related to a target domain and a
reverse diffusion bridge related to a source domain are cascaded to realize effective conversion from the source domain to the target domain; in the reasoning stage, target domain dominant information is reserved in the middle distribution track through a knowledge retention mechanism; and combining the real
data set and the conversion
data set, and realizing accurate fault diagnosis by using the trained model. As a plug-and-play
assembly for executing
submarine pipeline cross-domain fault diagnosis, the
submarine pipeline cross-domain fault diagnosis method has the following three important characteristics: (1) expandability: the application is wide, and the
submarine pipeline cross-domain fault diagnosis method is suitable for any type of data including time sequences or images; (2) flexibility: a channel from a target domain to a source domain is constructed by deconstructing and combining two completely independent
diffusion models, which means that an inverse bridge of any specific domain can be reused without retraining; and (3) conciseness: the robustness of the model can be effectively improved by
processing diversity of operation conditions introduced by a conversion sample.