Multi-modal multi-scale multi-level fusion quadrant entropy for mechanical fault diagnosis

Compared to single-sensor fault diagnosis models, multi-sensor information fusion models utilize potential fault information from various sensors for more precise fault diagnosis. However, most fusion models require many training samples to construct accurate models. Collecting these data is costly and challenging, increasing the time needed to build the training model. These models typically fuse information from multiple vibration sensors, with limited research on multi-modal information fusion, such as combining vibration and acoustic data. Additionally, the generality of existing models is weak, often requiring structural and parameter adjustments for different diagnostic tasks. To address these challenges, this paper proposes a high-accuracy and high-efficiency mechanical fault diagnosis model based on the multi-modal multi-scale multi-level fusion quadrant entropy (MMMFQE) using limited training samples. The proposed MMMFQE theory effectively constructs multi-modal information fusion feature maps across multiple scales and levels. The fusion quadrant entropy is then proposed to accurately characterize the mechanical states by analyzing the complexities of fusion feature maps. Analysis of two industrial datasets shows that the proposed model achieves 100% and 99.46% accuracy with only five training samples per state. Moreover, the accuracy, efficiency, and few-shot ability of the proposed model surpass those of several advanced models.