MTSNN: A Few-Shot Fine-Grained Diagnosis Framework for Cross-Machine Fault Diagnosis With Heterogeneous Fault Categories

Deep-learning (DL)-based intelligent fault diagnosis (IFD) methods are extensively used in mechanical systems. In practical industrial contexts, DL models are constrained by three primary limitations: 1) the performance of DL models heavily relies on a significant volume of high-quality labeled data during the training process; 2) the diagnostic capabilities of DL models are insufficient to handle the complex and variable conditions encountered in mechanical systems; and 3) current DL-based IFD approaches primarily focus on diagnosing coarse-grained faults, while less attention is given to fine-grained fault detection. In response to these limitations, a new meta-transfer spiking neural network (MTSNN) is introduced for cross-machine fine-grained fault diagnosis in mechanical equipment. First, a convolutional spiking neural network (CSNN) is constructed to extract the representative features from raw signals by leveraging the low energy consumption, native ability to process time-series data, and high robustness to noise of spiking neural network (SNN). Second, a novel comprehensive loss function called meta-prototype contrastive loss is proposed, which integrates prototypical learning (PL) loss, meta contrastive learning (MCL) loss, and entropy information maximization (EIM) loss, allowing the model to effectively transfer diagnostic knowledge across different machines in limited samples scenario. The experimental evaluation conducted on three datasets reveals that the proposed MTSNN significantly outperforms other methods in diagnosing faults, particularly when applied to different machines with heterogeneous fault categories.