The gas insulated transmission line (GIL), a crucial component of the power system, faces challenges in mechanical fault diagnosis including difficulties in quantifying sensor signal selection, balancing recognition accuracy, and model training speed. This paper proposes a GIL mechanical fault diagnosis method based on the feature fusion of multi-channel vibration sensor signals and an improved optimization extreme learning machine (ELM). Initially, three optimal signals are selected based on the correlation coefficients of multiple signals, and their time–frequency domain features are extracted. These features are then fused using neighborhood preserving embedding (NPE) and input into the improved optimized ELM model. The enhanced optimization algorithm reduces the likelihood of falling into local optima, thereby improving the model’s recognition performance. Experimental results demonstrate that this method effectively enhances the recognition accuracy of mechanical faults in GIL equipment and enables rapid diagnosis.