Non-stationary signal analysis based on general parameterized time–frequency transform and its application in the feature extraction of a rotary machine |
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作者姓名: | Peng ZHOU Zhike PENG Shiqian CHEN Yang YANG Wenming ZHANG |
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摘 要: |
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Non-stationary signal analysis based on general parameterized time--frequency transform and its application in the feature extraction of a rotary machine |
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Authors: | Peng Zhou Zhike Peng Shiqian Chen Yang Yang Wenming Zhang |
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Affiliation: | School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 201100, China |
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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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Keywords: | rotary machines condition monitoring fault diagnosis GPTFT SCI |
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