CFSPT: A lightweight cross-machine model for compound fault diagnosis of machine-level motors

The inevitable multi-component assembly errors and complex data collection sites lead to coupling fault information and global distribution differences among individuals, making fault diagnosis of machine-level motors more challenging. This article proposes a lightweight cross-machine model, namely, coarse-fine signal pruning transformer (CFSPT), specially for the compound fault diagnosis. Specifically, the unidirectional multi-scale convolutional patches (UDMCP) are proposed to provide flexible global information interaction and fusion. Coarse-grained temporal locator (CTL) and pruned fine-grained feature extractor (PFFE) are designed as the multi-process feature pruner and extractor, which not only improve attention to key temporal blocks, but also achieve lightweight design. The superiority of the proposed CFSPT is validated on real industrial production line motors instead of laboratory part-level signals. The comprehensive experimental results based on visualization show that the proposed method achieves the highest generalization performance of 94.74% cross machine accuracy (CMA). The proposed CFSPT with interpretable design, as a lightweight, efficient and reliable method, has great application potential in cross machine fault diagnosis scenarios of machine-level motors.