In order to diagnosis mechanical faults in gas insulated transmission line (GIL) effectively, the 110 kV GIL experimental platform is built and three typical mechanical faults are designed. The joint method of complementary ensemble empirical mode decomposition (CEEMD) fuzzy entropy and whale optimization algorithm-extreme learning machine (WOA-ELM) model is used to identify and diagnose the GIL mechanical fault mode. Firstly, the CEEMD method is used to decompose the vibration signal, and the positive and negative white noise is introduced to process the signal to obtain the modal component with fault information. Secondly, the fuzzy entropy is used to calculate the eigenvalues of modal component to obtain the fuzzy entropy which can represent the fault features. Finally, the WOA-ELM model is used to recognize the eigenvector set, and the clustering results and adaptive threshold are used to analyze the fault features. The mechanical fault of GIL equipment can be diagnosed and forewarned by using the value. The results show that, combining CEEMD and fuzzy entropy to analyze GIL vibration signal can effectively avoid the interference of modal aliasing and redundant noise components, and obtain the eigenvalues that can characterize the fault characteristics; using WOA-ELM model can effectively realize GIL equipment mechanical fault diagnosis and early warning.