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

作者：Zhou, Peng; Peng, Zhike^*; Chen, Shiqian; Yang, Yang; Zhang, Wenming

来源：Frontiers of Mechanical Engineering, 2018, 13(2): 292-300.

DOI：10.1007/s11465-017-0443-0

• rotary machines
• condition monitoring
• fault diagnosis
• GPTFT
• SCI

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.

• 出版日期2018-6
• 单位上海交通大学