A cross-domain bearing fault diagnosis method under sample imbalance based on multi-view information cross-integration ensemble learning

Reliable cross-domain bearing fault diagnosis is crucial for rotating machinery health monitoring in complex industrial systems. However, practical applications often face severe sample imbalance and domain distribution shifts, the combination of which frequently causes the model to be biased toward majority classes. To overcome these challenges, we propose multi-view information cross-integration ensemble learning (MICE). Firstly, MICE employs a multi-view cross-integration strategy to generate mixed signals, thereby extracting complementary feature representations while reducing redundancy. Secondly, to handle sample imbalance, a consistency voting entropy optimization strategy analyzes prediction scores to set dynamic thresholds and generate high-confidence pseudo-labels. This process enables the model to effectively capture critical minority-class features. Finally, a dual-path collaborative learning mechanism integrates inter-view feature transfer with inter-domain alignment, and employs a confusion-class focusing strategy to explicitly distinguish confusable categories. Extensive experiments on two public datasets validate the superiority and strong cross-domain generalization capability of the proposed MICE method.