Multi-stream domain adversarial prototype network for integrated smart roller TBM main bearing fault diagnosis across various low rotating speeds

The well-being of tunnel boring machines (TBM) heavily relies on the health status of main bearings, significantly impacting both safety and operational efficiency. Investigating the operation monitoring and diagnosis algorithms of TBM main bearings in practical scenarios with limited fault data and real-time variations in low-speed heavy-load operating conditions poses a research challenge. To solve these issues, a novel domain-adversarial prototype network diagnostic algorithm with a multi-stream fusion feature encoder (MDAPN) based on smart roller monitoring data is proposed. In contrast to existing external vibration monitoring schemes for bearings, monitoring data from smart rollers can mitigate complex external interference and greatly shorten the transmission path of fault sources. The algorithm encompasses a multi-stream fusion feature encoder, domain discriminator, and prototype network classifier. Specifically, the multi-stream fusion feature encoder adopts the two-stream convolutional network to deeply extract the axial-radial fused features of rollers, incorporating shallow statistical feature information. Domain-invariant features are generated based on domain adversarial learning strategies, and the prototype network classifier reduces dependence on target domain samples. An integrated smart roller main bearing fault simulation testbed was built, conducting 6 sets of cross-domain experiments. The proposed method reached an average accuracy of 98.41 % under 5-shot, surpassing the baseline method by 6.57 % at least. This validates that the MDAPN based on smart roller state exhibits excellent fault recognition performance for the main bearing under varying operational conditions with scarce available samples.