An Intelligent Fault Diagnosis Method that Combines Operating Condition Attribute Encoding and Multi-scale Cascading Concept

Abstract

Abstract In order to improve the generalization ability of the model under different working conditions and the robustness of intelligent fault diagnosis, and learn a broader feature representation, this paper proposes an intelligent fault diagnosis method that integrates working condition attribute encoding and multi-scale cascade concepts. This method integrates working condition information into vibration data by introducing methods such as working condition attribute coding, multi-scale cascade modules and quadruple losses, and effectively extracts invariant features. this method trains the fault classification model through a three-stage training process. Finally, the objective is to accomplish fault diagnosis in diverse operational scenarios. Experimental results show that this method improves the fault diagnosis accuracy across diverse operational conditions, indicating that the model has good generalization ability.