Semantic-guided orthogonal subspace network for class-incremental continual intelligent fault diagnosis

Abstract

Long-term operation of industrial equipment results in the continuous accumulation of monitoring data for emerging fault types. This phenomenon leads to catastrophic forgetting in intelligent diagnostic models and degrades overall performance. A semantic-guided orthogonal subspace network (SOSN) is proposed for class-incremental continual intelligent fault diagnosis. The architecture utilizes lightweight adapters to construct task-specific orthogonal subspaces within a frozen pre-trained backbone. These subspaces ensure that gradient updates for novel tasks remain theoretically isolated from historical parameters. To reduce the dependency on historical raw data, a semantic-guided prototype synthesis mechanism is developed. This mechanism reconstructs historical class centers by leveraging feature similarity across different task subspaces. Extensive evaluations are performed on rolling bearing datasets and a joint heterogeneous dataset comprising wheelset bearings and elevator door systems. Experimental results demonstrate that SOSN significantly outperforms mainstream class-incremental learning approaches in diagnostic accuracy. The framework effectively balances representational plasticity and diagnostic stability across diverse mechanical systems.