Intelligent real-time perception method for rock strength based on vibration and power fusion characteristics

Abstract

To improve the sensing accuracy of rock strength parameters, a method based on the fusion of vibration response and electrical response is proposed. Firstly, multi-layer noise reduction based on local mean decomposition is carried out for vibration response, and the product function containing exceeds 85% of the original information is selected by the multi-index comprehensive decision method, and the wavelet threshold denoising is used as the pre-processing unit of singular value decomposition. Next, the multi-domain feature extraction and KPCA are carried out for vibration signal after noise reduction to construct fusion feature vectors. Also, the statistical characteristics of the electrical parameters are extracted and fused with the fusion features. Finally, multi-source information is input into the least squares support vector machine model optimized by the improved whale algorithm to perceive the rock strength parameters. The experimental results show that the sensing accuracy of the proposed method is the highest (96.875%) compared with the single information while drilling and the traditional prediction method, which verifies the superiority of the proposed method.