Intelligent Gearbox Fault Diagnosis Under Variable Load Conditions via Zero‐Shot Learning

Deep learning has demonstrated remarkable potential for intelligent gearbox fault diagnosis; however, its effectiveness remains constrained by two critical challenges: the scarcity of labeled fault samples and the degradation of diagnostic accuracy under nonstationary operating conditions. Conventional supervised approaches rely heavily on abundant labeled data, which are often difficult or costly to obtain due to the rarity of severe fault occurrences. Moreover, variable load and speed fluctuations introduce nonstationary characteristics that impair the reliability of feature extraction and model generalization. To overcome these limitations, this study proposes a novel infrared thermal vision and acoustics‐driven multimodal zero‐shot diagnosis network (IRT‐AMZS). The framework introduces a multireceptive field‐based cross‐modal fusion (MRCF) module that adaptively extracts and integrates complementary fault features from infrared thermal and acoustic signals, enhancing robustness under varying operational conditions. Furthermore, by employing semantic fault prompts, the IRT‐AMZS framework effectively transfers diagnostic knowledge from observed to unobserved fault categories, thereby enabling accurate diagnosis even in the absence of labeled samples for unseen faults. Experimental evaluations on variable‐load gearbox datasets demonstrate that IRT‐AMZS significantly improves diagnostic accuracy and generalization compared to competing methods.