Maximizing Incipient Fault Signatures of Rotating Machines Using Wavelet Entropy and Cyclic Logarithmic Envelope Spectrum

摘要

This paper presents a constant speed rotating machines fault analysis method to maximize incipient fault signatures based on computed order tracking with adaptive filter and cyclic logarithmic envelope spectrum (CLES) tools. First, adaptive filter reduces not-concerned harmonic components and measurement artefacts from a vibration signal. To accurately and precisely select the step size in the process of the LMS-type adaptive filter, a new method is proposed based on wavelet entropy (WE). Then, a tacho signal is generated from vibration signal and computed order tracking is performed using this tacho signal. Finally, to reduce the effect of second-order cyclostationarity, CLES tool is used to analyze the incipient fault features. Simulations are carried out and the performances of WE and CLES are compared with traditional method. Furthermore, vibration signals of real experimental data are used to verify the robustness of the proposed method. The experimental results show that the proposed method can effectively maximize the incipient fault signatures of rotating machines.