A carrier wave extraction method for cavitation characterization based on time synchronous average and time-frequency analysis. (22nd December 2020) - Wu, Kelin; Xing, Yun; Chu, Ning; Wu, Peng; Cao, Linlin; Wu, Dazhuan
Highlights: Cavitation characterization is realized by TSA and time-frequency analysis (TATF). Extraction of carrier wave components caused by cavitation is the key issue. TAFT exhibits better carrier wave extraction performance than STFT and WT. Abstract: Cavitation detection is important in ensuring the reliability of fluid machinery, such as pumps. Vibration signal analysis is widely accepted as an effective tool in condition monitoring and fault diagnosis due to its ability to obtain substantial information and convenience of sensor arrangement. However, cavitation characterization based on vibration measurement is challenging because of the complicated underlying mechanism and low signal-to-noise ratio (SNR) of actual data. This study proposes a carrier wave extraction method for cavitation characterization by combining time synchronous average and time-frequency analysis (TATF) based on amplitude-modulated (AM) signal theory. The proposed method can reasonably measure cavitation severity by distinguishing time-frequency characteristics between different cavitation states. Compared with traditional vibration/acoustic signal monitoring or intelligent diagnostic techniques, cavitation detection based on TATF has the advantages of accurate classification and outstanding physical significance. First, cavitation state division criterion based on energy indicator is proposed. Its superiority is verified via comparison with the traditional criterion of hydraulic head. Second, the vibration signal model of pumps is established as an AM signal model, and the modulation mechanism is elaborated. Extraction of carrier wave components caused by cavitation is regarded as the critical issue in cavitation characterization. Then, TATF is described detailedly and its effectiveness is validated by simulation signals and actual data. Finally, the intelligent classification results of cavitation state by deep convolutional neural network (DCNN) demonstrate the superiority of TATF over short-time Fourier transform (STFT) and wavelet transform (WT).