Fault Feature Extraction Method of Ball Screw Based on Singular Value Decomposition, CEEMDAN and 1.5DTES

Abstract

In this article, a method is proposed to effectively extract weak fault features and accurately diagnose faults in ball screws, even in the presence of strong background noise. This method combines singular value decomposition (SVD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the 1.5-dimensional spectrum (1.5D) to process and analyze fault vibration signals. The first step involves decomposing the fault signal using the SVD algorithm. The singular values are then screened, and the part of the screen containing more noise information is extracted to complete the first denoising step. The second step involves decomposing the signal after the initial denoising process using CEEMDAN and removing some of the false components from the intrinsic mode function (IMF) components, based on the kurtosis correlation function index. The signal is then reconstructed to complete the second denoising step. Finally, the denoised signal is analyzed using Teager energy operator demodulation and 1.5D spectral analysis to extract the fault frequency and determine the location of the fault in the ball screw. This method has been compared with other denoising methods, such as wavelet packet decomposition combined with CEEMDAN or SVD combined with variational mode decomposition (VMD), and the results show that under the condition of strong background noise, the proposed method can better extract the fault frequency of ball screw.

Keywords

• strong background noise
• ball screw
• SVD
• CEEMDAN
• 1.5D
• faults