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Frontiers of Mechanical Engineering >> 2018, Volume 13, Issue 2 doi: 10.1007/s11465-017-0443-0

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

School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 201100, China

Accepted: 2017-07-06 Available online: 2018-03-19

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Abstract

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.

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