| Summary: | (1) Background: with the development of intelligent transportation, effectively collecting and identifying the working state of vehicles is conducive to the analysis and processing of vehicle information by internet of vehicles, so as to reduce the occurrence of traffic accidents. Aiming at the problem of low identification accuracy of the mechanical vibration fault signal, a signal identification method based on time-frequency detection is introduced; (2) Methods: this paper constructs a parameter model of the synchroextracting S transform on the basis of the poor time-frequency concentration of the original S transform; (3) Results: in the case of SNR = −5~+30 dB, compared with other transformations, the Rényi entropy value of <i>SEST</i> is the smallest, and the Rényi entropy value is 0.5246 when SNR = +22 dB; (4) Conclusions: through simulation comparison and analysis, the excellent time-frequency concentration and anti-noise characteristics of the <i>SEST</i> are highlighted, and the rotor vibration fault signals such as rotor misalignment, unbalance and bearing wear are identified by <i>SEST</i>.
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