The diagnosis of ceramic bearings based on the fusion of heterogeneous data from multiple sources can reflect fault information from multiple dimensions, representing an advanced method of fault diagnosis. Nevertheless, the extant cross-domain fault diagnosis methods for ceramic bearings based on heterogeneous data fusion are constrained by the dimensionality of the data, the network’s inadequate capacity to comprehend the interrelationships between the data, and the inconsistency of the fused fault features, which impede cross-domain fault diagnosis for ceramic bearings. In this paper, we propose a meta-learning network based on the fusion of heterogeneous data from vibration, sound, and infrared sources for cross-domain fault diagnosis of ceramic bearings under variable operating conditions. First, the non-parametric encoding of Gram Angle Field is employed to align and fuse the one-dimensional and two-dimensional heterogeneous data dimensions, thereby addressing the issue of dimensional disparity inherent in heterogeneous data. Second, a multi-source feature reconstruction module is designed to enhance the network’s ability to comprehend heterogeneous data features. To improve fault feature consistency, a multi-channel feature consistency module is developed to facilitate more consistent and generalizable feature representation. The results of the experiments demonstrate that the method attains an average diagnosis accuracy of 98.91% across six distinct cross-domain scenarios and exhibits robust performance in noise resistance experiments. The proposed method combines meta-learning with multimodal data fusion to address the limitations of single-modal sensing of rotating machinery under multi-physical field coupling conditions. It provides some practical application value for multimodal data monitoring of rotating systems such as aero-engines, high-speed machine tool spindles, and wind power gearboxes.
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