Gearbox fault diagnosis method based on bird flock algorithm and hidden Markov model

Abstract

The invention discloses a gearbox fault diagnosis method based on a bird flock algorithm and a hidden Markov model. The gearbox fault diagnosis method comprises four steps of: feature extraction, model parameter initialization, parameter training, and output probability calculation. The step (1) feature extraction is implemented by selecting a wavelet function for performing 3-layer wavelet packetdecomposition and reconstruction on vibration signals, and analyzing wavelet decomposition coefficient signals of each frequency band so as to realize the extraction of feature information of different fault states represented by the vibration signals from each frequency band respectively. The step (2) model parameter initialization is implemented by taking frequency band energy of the vibrationsignals as eigenvectors for modeling. The step (3) parameter training is implemented by adopting the bird flock algorithm for re-estimation according to the parameters initialized in the second step.The steps (4) output probability calculation is implemented by extracting monitored vibration data features after models are constructed in the previous step, substituting the monitored vibration datafeatures into different fault state models, calculating an output probability by adopting a forward-backward algorithm, and regarding the maximum probability as a corresponding fault type.

Description

technical field [0001] The invention relates to the field of fault diagnosis of a wind turbine gearbox, in particular to a gearbox fault diagnosis method based on a bird swarm algorithm and a hidden Markov model. Background technique [0002] Gear transmission has the advantages of stable transmission, reliability, high efficiency, long life, precise transmission ratio, and large power range. However, due to its complex structure and harsh working environment, gears are prone to failure, which leads to failure of the entire system. According to relevant literature statistics, 80% and 10% of the failures of transmission machinery and rotating machinery are gearbox failures respectively. With the automation, complexity and large-scale of equipment systems. The failure of gears has caused more and more losses to the entire industrial production and social life. For example, the condition detection and fault diagnosis of the gearbox can fundamentally change the status quo of aft...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a gear box fault diagnosis method based on the bird swarm algorithm and hidden Markov model, which realizes the simulation analysis of the gear box fault through vibration analysis and the improved Gaussian mixture hidden Markov model , and at the same time, in order to solve the problem that hidden Markov model parameter training tends to fall into local convergence

Images