Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

摘要

With the development of large rotary machines for faster and more integrated performance,the condition monitoring and fault diagnosis for them are becoming more challenging.Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature,the methods based on TF analysis have been widely-used to solve these two problems in the industrial community.This article introduces an effective non-stationary signal analysis method based on the general parameterized time-frequency transform (GPTFT).The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform.This method can produce a high-concentrated TF pattern with a general kernel.A multi-component instantaneous frequency (IF) extraction method is proposed based on it.The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI).Moreover,such an IF estimation process is iteratively operated until all the components are extracted.The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.