Study on the adaptive wavelet threshold denoising method for coal mine hoisting wire rope signals based on novel thresholding function

Abstract

The preprocessing of wire rope online testing signals is important for quantifying any wire rope defects. As existing signal denoising methods are inadequate, the present study proposes an adaptive wavelet threshold denoising method based on a novel thresholding function. First, the adaptive denoising method for a coal mine hoisting wire rope magnetic flux leakage testing signal is analysed. Then, the adaptive denoising method for the wire rope magnetic flux leakage testing signal is studied based on Stein's unbiased risk estimate (SURE). On this basis, a new threshold function adapted to the testing signal features of the wire rope magnetic flux leakage is designed. The new function not only has a high-order continuous derivative but also no constant bias, so that it can be run in adaptive iterative form to dynamically find the best threshold. Finally, this work experimentally verifies the new method and the results show that it can effectively identify seven defects that totally conform to the real situation with a signal-to-noise ratio that is 25% higher than that of the traditional method, which also missed two defects. The new method can be more effectively used for denoising.