An Energy-Efficient Mechanical Fault Diagnosis Method Based on Neural Dynamics-Inspired Metric SpikingFormer for Insufficient Samples in Industrial Internet of Things

The industrial Internet of Things (IIoT) significantly enhances mechanical fault diagnosis. However, IIoT-based intelligent diagnostic models struggle with sample insufficiency and high energy consumption due to collection costs and limited computing resources. Therefore, this article proposes an energy-efficient mechanical fault diagnosis method based on the neural-dynamics-inspired metric SpikingFormer (MSF) to achieve accurate fault recognition under insufficient samples. The design and construction of a data acquisition system based on the aircraft engine platform and the ship water jet propulsion platform effectively support the operation of the developed diagnostic algorithm. Specifically, an event-driven multiscale mask spiking self-attention (MMSSA) mechanism is designed to focus critical spatiotemporal features from different scales under low computational complexity. Meanwhile, a rate encoding metric classifier (REMC) is constructed to bridge spiking learning and prototype representation, thereby accurately classifying fault under insufficient samples. Finally, a customized backpropagation strategy based on neural dynamics is developed to enable the MSF to learn effectively and be stable. The superiority of the MSF in energy consumption and diagnostic accuracy is verified through comparison with six authoritative methods across standard, laboratory-acquired, and real-world datasets. The results showed that the parameter count of MSF is $7.04\times $ and $20.46\times $ less than the strong baseline method, respectively, and the diagnostic accuracy on the two real datasets is 4.52% and 6.91% higher than the latest method, respectively.