# Cross-Domain Fault Diagnosis of Rotating Machinery Under Time-Varying Rotational Speed and Asymmetric Domain Label Condition

## TL;DR

This paper introduces a new method for fault diagnosis in rotating machinery that works even when fault labels differ between domains and speeds change.

## Contribution

The novel ASY-WLB method addresses asymmetric domain label spaces and time-varying speeds in fault diagnosis.

## Key findings

- The proposed ASY-WLB method improves diagnostic performance under asymmetric label conditions.
- Angular resampling effectively mitigates the impact of time-varying speed fluctuations.
- Experiments on two datasets show the superiority of the new method.

## Abstract

In practical engineering, the asymmetric problem of the domain label space is inevitable owing to the prior fault information of the target domain being difficult to completely obtain. This implies that the target domain may include unseen fault classes or lack certain fault classes found in the source domain. To maintain diagnostic performance and knowledge generalization across different speeds, cross-domain intelligent fault diagnosis (IFD) models are widely researched. However, the rigid requirement for consistent domain label spaces hinders the IFD model from identifying private fault patterns in the target domain. In practical engineering, the asymmetric domain label space problem is inevitable, as the target domain’s fault prior information is difficult to completely obtain. This means that the target domain may have unseen fault classes or lack some source domain fault classes. To address these challenges, we propose an asymmetric cross-domain IFD method with label position matching and boundary sparse learning (ASY-WLB). It reduces the IFD model’s dependence on domain label space symmetry during transient speed variation. To integrate signal prior knowledge for transferable feature representation, angular resampling is used to lessen the time-varying speed fluctuations’ impact on the IFD model. We design a label-positioning information compensation mechanism and weighted contrastive domain discrepancy, accurately matching unseen class label information and constraining the diagnosis model’s decision boundary from a data conditional distribution perspective. Finally, extensive experiments on two time-varying speed datasets demonstrate our method’s superiority.