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.