Abstract: The present disclosure discloses a nonlinear sparsity-based instantaneous dynamic frequency fault diagnosis method for an aviation intermediate bearing. In the method, a vibration signal and a rotating speed signal of high and low voltages of the intermediate bearing are acquired, and a vibration signal fragment x under a specific working condition is intercepted according to the rotating speed signal; a nonlinear sparse time-frequency enhancement model or a nonlinear sparse enhancement algorithm model is established based on derivative window function short-time Fourier transform of the vibration signal; the nonlinear sparse time-frequency enhancement model is solved or the nonlinear sparse enhancement algorithm model is improved by using a fast iterative shrinkage threshold algorithm and combining a k sparse strategy, and finally a nonlinear sparse time-frequency representation result {circumflex over (N)}x, may be obtained through iterative optimization.

Abstract: A fault detection method for a rotating machine based on sparse time synchronous averaging is disclosed, and the method includes: collecting a vibration signal and a rotating frequency or rotating frequency pulse signal of the rotating machine and performing analog-to-digital conversion to obtain the vibration signal and rotating speed information by a sensor; according to the type and number of detection components in the rotating machine, constructing a component-aware comb vector g based on the vibration signal and rotating speed information, wherein the type includes the gear, rotor and bearing; constructing a quasi-time synchronous average vector w based on the component-aware comb vector g; constructing a sparse time synchronous averaging model F by using the quasi-time synchronous average vector w; solving the sparse time synchronous averaging model F with an optimization solution algorithm to obtain a sparse spectrum and a reconstruction time signal.