Local type fault diagnosis method of rotating machine based on sparse decomposition optimization algorithm

The invention discloses a local type fault diagnosis method of a rotating machine based on a sparse decomposition optimization algorithm, which comprises the following steps: S1, acquiring a rotatingmachine vibration acceleration response signal containing fault characteristic information; S2, intercepting a signal with an appropriate length, performing noise reduction pretreatment by applying ahigh-pass filter and singular value decomposition, and improving the signal-to-noise ratio; S3, setting an atom optimization criterion, and establishing a time-frequency domain correlation coefficientconstraint function of atoms and an actually-measured signal; S4, updating parameters of an initial random dictionary by using a particle swarm hybrid gradient descent algorithm to obtain an optimized impact response dictionary; S5, solving a sparse coefficient by using a segmented Lagrange contraction algorithm, reconstructing the fault characteristic signal according to the coefficient and thedictionary; S6, analyzing the impact response time interval of the reconstructed signal and demodulation of the characteristic frequency of the amplitude spectrum, identifying the position of the fault to finish the fault diagnosis. The dictionary has the advantages of high precision, high speed, stronger noise resistance and more accurate reconstruction characteristic signal.