Abstract:We combine improved complete empirical mode decomposition with adaptive noise (ICEEMDAN) and distributive entropy(DistEn) to propose a method to suppress the random noise of the extensometer signal without customizing the parameters and with good denoising effect. Firstly, the signal of the extensometer is processed by ICEEMDAN, and several intrinsic mode functions (IMF) are obtained.Then the distributive entropy value of each IMF component is calculated, and according to the magnitude of different distributive entropy values and the degree of chaos of the characterized component signals, each IMF is targeted to be traded off. Finally, linear reconstruction is performed. Designing simulated signal denoising experiments and SS-Y extensometer signal denoising experiments, the results show that the scaler signal reconstruction based on the ICEEMDAN-DistEn denoising model has significantly better reduction and denoising effect than several denoising models such as CEEMDAN-DistEn, wavelet denoising and Kalman filtering.