The assembly process of the variable stator vane (VSV) adjusting mechanism of aero-engine requires manual detection of the anti-loosening wire assembly correctness of the connecting rod

which is inefficient and errorprone. An intelligent fault-detection method based on multi-model cascade is proposed to replace the manual detection operation. The method is a model integration of multiple convolutional neural networks

which consists of three parts: detection module

classification module

and post-processing of comparison fault detection. Firstly

the depthwise separable convolution with lightweight decoupling head mixing different sizes of convolutional kernels is proposed on the detection module to improve YOLOv5s

and the improved YOLOv5s achieves an average accuracy of 97.9% on the test set

which is improved by 3.4% and 1.5% compared to YOLOv5s and YOLOv8s

respectively. Secondly

the ConvNeXt classification head is improved by using 7×7 deep convolution instead of global average pooling on the classification module

and the performance is improved

reaching an accuracy of 97.5% and 95.4% on the connecting rod dataset and the thread dataset

respectively. Finally

the results of the two classification models are matched in the post-processing module to obtain the assembly detection result. The intelligent fault-detection method is verified by the image dataset collected from the field assembly workshop

and the results show that the average precision of the proposed method reaches 92.7%

which further verifies the reliability of the proposed method.