Fault Diagnosis of Intershaft Bearing without Key-phase Signal Driven by Multi-component Synchroextracting Transform

doi:10.3901/JME.2024.16.083

Abstract

Abstract: The intershaft bearing is a failure-prone part of the aero-engine. Under the influence of complex structure such as elastic support and thin-walled magazine, the intershaft bearing failure characteristics in the vibration signals measured by the sensor mounted on the casing are extremely weak, and because the equipment is in variable working condition for a long time, there is no high and low pressure speed signal which is completely synchronized and accurate with the vibration signal, the non-linear time-varying characteristics of the vibration signal also interferes seriously with the failure diagnosis of intershaft bearing. To solve the above problems, this paper proposes a fault diagnosis method of intershaft bearing without key-phase signal driven by multi-component synchroextracting transform(SET). Firstly, SET is used to eliminate the redundant time-frequency energy in the time-frequency diagram, and the instantaneous rotational frequency of the inner and outer rings of the intershaft bearing is extracted from the same group of vibration signals with low SNR. The original vibration signal is then subjected to minimum entropy deconvolution(MED) and infogram to enhance the weak fault features influenced by the transmission path and environment, MED optimizes the spectral negative entropy results in the infogram while highlighting the fault pulse, and the optimal resonance demodulation band is selected more accurately. Finally, the difference of the extracted rotational frequency results is used as the reference in the rotation speed difference domain for order tracking and envelope analysis. The simulation and experimental analysis results show that the method can effectively extract the fault characteristic order of intershaft bearings without key-phase signal. It can realize the fault diagnosis of intershaft bearings, and has obvious advantages over typical signal processing methods.

Key words: intershaft bearing, variable working condition, synchroextracting transform, minimum entropy deconvolution, infogram

CLC Number:

TG156

YANG Yang, HU Minghui, JIANG Zhinong, LU Ziyuan. Fault Diagnosis of Intershaft Bearing without Key-phase Signal Driven by Multi-component Synchroextracting Transform[J]. Journal of Mechanical Engineering, 2024, 60(16): 83-97.

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