Frontiers of Mechanical Engineering >
Non-stationary signal analysis based on general parameterized time--frequency transform and its application in the feature extraction of a rotary machine
Received date: 28 Nov 2016
Accepted date: 19 Feb 2017
Published date: 16 Mar 2018
Copyright
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.
Key words: rotary machines; condition monitoring; fault diagnosis; GPTFT; SCI
Peng ZHOU , Zhike PENG , Shiqian CHEN , Yang YANG , Wenming ZHANG . Non-stationary signal analysis based on general parameterized time--frequency transform and its application in the feature extraction of a rotary machine[J]. Frontiers of Mechanical Engineering, 2018 , 13(2) : 292 -300 . DOI: 10.1007/s11465-017-0443-0
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