A Diagnosis-Based Siamese Network for Fault Detection Through Transfer Learning.

ABSTRACT: Traditional deep-learning-based approaches often struggle with data imbalance and variability across fault conditions and normal scenarios, especially in industrial processes. Besides, inconsistent feature distributions from combining different fault conditions into the same category are a limitation for many data-driven algorithms. This study proposes a fault detection framework that combines Siamese neural networks with transfer learning, using a pretrained fault diagnosis model as its backbone, taking advantage of knowledge related to the attribute space that characterizes individual fault patterns. Our method transforms the detection classification problem into an embedding similarity task, allowing for improved differentiation between normal and faulty operations. This approach poses

SUBMITTER: Neto JG 

PROVIDER: S-EPMC12264958 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature