Open Access

Lv WangORCiD, Junyu QiORCiD, Rui Tang, Qijun WenORCiD, Yi QinORCiD

• Current zero-shot fault diagnosis methods employ a direct mapping strategy between the signal feature and attribute prototype, and use nearest neighbor estimation as the metric. This mapping-metric approach can lead to the hubness problem, i.e., some samples of a prototype are recognized as other prototypes due to the nearest-neighbor estimation, affecting the diagnostic accuracy. To tackle this problem, a prototype expansion mapping method is constructed. A novel kernel function is proposed to expand prototype dimensions and increase the distance between prototypes, overcoming the hubness problem. Its ability to alleviate the hubness problem is verified through the intuitive illustration and theoretical analysis. Furthermore, since the existing metrics are not suitable for evaluating the hubness problem in zero-shot diagnosis scenarios, a new evaluation method is designed to evaluate the degree of the hubness problem. Moreover, a new attribute prototype definition approach is designed to mine the additional fault characteristics, enhancing zero-shot diagnostic capability. Building upon these innovations, we develop the zero-shot prototype expansion (ZSPE) model for compound fault diagnosis in rotating machinery. Experimental validation on bearing compound faults demonstrates ZSPE's ability to diagnose unseen compound faults without requiring any compound fault training samples, offering a solution to data acquisition challenges in real-world engineering contexts.