A Lightweight Triple-Stream Network With Multisensor Fusion for Enhanced Few-Shot Learning Fault Diagnosis

Abstract

The application of multiple sensors significantly enhances the accuracy of industrial fault diagnosis, but existing algorithms are structurally complex and rely heavily on extensive training data. To optimize the efficiency of diagnosis, this article proposes a lightweight time-frequency-statistical domain fusion network. The model comprises three data streams that analyze the time-domain, frequency-domain, and statistical features of vibration signals, employing an improved channel attention mechanism for weighted fusion. In addition, two model-agnostic few-shot enhancement strategies are introduced, aiming to improve accuracy where training samples are scarce by reducing signal sample variations and optimizing the distribution of signals in the feature space. By combining these techniques, the proposed method exhibits superior performance in few-shot learning on two datasets compared to other multisensor fusion methods, while also achieving higher computational speed. The results of this research are of significant importance in enhancing the fault diagnostic capabilities of multisensor systems in practical industrial applications.