Research On Fault Diagnosis Of Wind Turbine Gearbox Based On Resonance-based Sparse Signal Decomposition

In recent years,with the increasing maturity of wind power technology,the installed capacity of wind turbines has increased year by year.This is followed by higher demands on the reliability of wind power equipment.How to realize wind turbines with lower maintenance costs while maintaining high power generation efficiency has become the focus of the healthy development of the wind power industry.Gearbox as the most important core component of the power transmission equipment for wind turbines,it is of great practical significance to perform accurate fault detection on it.This thesis takes the fault feature extraction of the gearbox vibration signal as the research object,and uses the Resonance-based Sparse Signal Decomposition(RB-SSD)and Maximum Correlated Kurtosis Deconvolution(MCKD)method to realize the enhanced extraction of the fault signal of the gearbox fault signal.Then,based on the excellent harmonic characteristics of the high resonant component of the vibration signal,the time-frequency analysis method is used to estimate the rotational speed of the rotating equipment.In the next step,through the angular re-sampling technique,the low-resonance component,which contains a lot of fault impact,of the variable-speed vibration signal is processed in the angular domain.Finally,the fault pulse is extracted through the MCKD at variable speed.In this thesis,the theoretical research,experimental analysis and engineering practice are taken to verify the proposed method,and the result show that this method is practical and effective,which provides a new idea for fault diagnosis of wind power gearbox.The specific work of this thesis is as follows:1)Analyzing the structure characteristics,failure mechanism and vibration fault characteristics of the gearbox.Aiming at the fault characteristics of rotating machinery,an enhanced extraction method for periodic pulse is proposed.Which combines the advantages of RB-SSD in pulse signal extraction and the MCKD method for periodic pulse-sensitivity.2)Because of perfect harmonic characteristics of the high-resonance component,the Synchrosqueezing Wavelet Transform(SWT)is used to process it to achieve rotational speed estimation for rotating machinery.Based on this estimated speed,the low-resonance components,which contains a large number of pulses,are re-sampled in the angular domain so that their pulses have periodicity in the angular domain.Finally,the maximum correlation kurtosis is used to achieve the purpose of enhanced fault pulse extraction under variable speed conditions.3)In order to verify the theoretical method proposed in the thesis,we set up a Wind power failure simulation experiment platform.Then by designing the corresponding simulation experiment of gearbox faults to completely verify the proposed method.Afterwards,the feasibility of the proposed method is further proved by analyzing the engineering measured data.