Abstract：Motors play a critical role in industrial production，yet their long-term operation under complex and

variable conditions poses significant fault challenges. To address this，a convolutional neural network（CNN）

architecture with BCA-ISA collaborative optimization was proposed，featuring a three-stage progressive design：

firstly，a composite enhancement strategy integrating multi-scale window shifting and noise superposition for 1D

time-series data was implemented，improving model generalization capability by 10% under identical dataset

conditions. Secondly，targeting parameter solidification issues，innovatively combined the global search

characteristics of the bee colony algorithm（BCA）with CNN's local feature extraction capabilities. This established

an adaptive parameter-space mapping mechanism enabling self-optimizing configuration under complex operating

conditions，achieving a 16.2% acceleration in parameter adjustment compared to traditional gradient descent

methods. Finally，a collaborative structure integrating large kernels，bottleneck layers，and improved self-attention

（ISA）mechanisms was designed：the large kernel + bottleneck layer combination resolved high-frequency feature

capture inefficiencies through expanded receptive fields and feature compression. The ISA module overcomed longrange dependency modeling bottlenecks via positional weighting and gradient gating mechanisms experimental validation confirms a 31% enhancement in high-frequency fault feature recognition versus baseline models.

        Key words：motor；fault diagnosis；convolutional neural network（CNN）；attention mechanism；bee colony algorithm（BCA）

     Format Citation:曹鑫辉，胡庆军，冯宇,等. 基于CNN⁃BCA⁃ISA的电机故障诊断研究[J].电气传动,2026,56(10):89-96. CAO Xinhui，HU Qingjun，FENG Yu,et al. Research on motor fault diagnosis based on CNN-BCA-ISA [J].Electric Drive, 2026,56(10):89-96