Research On Megawatt Wind Generator Gearbox Health State Prediction Based On Vibration Signal

Wind energy, as a sustainable clean energy, has been used as an important energysource for the sustainable development strategy in many countries. Wind turbine is theequipment that converts wind energy into electricity. Gearbox, as the critical mechanicalsystem of wind turbine, determines the stability of wind turbine. There are some commonfailure of the gear box during operation, such as gear broken，surface pitting，bearingfailure，shaft bending and so on，which seriously affect the function of wind turbine.The fault signal generated by the gear box is usually non-stationary vibration signaland also usually disturbed by other noise generated by wind turbine. It will more difficultto achieve the recognition of fault state when several failures occur in same time.The thesis based on the vibration mechanism and vibration characteristics of the gearand bearing of wind turbine, with the BP neural network, the intrinsic mode functionsthrough empirical mode decomposition is obtained using the method of Hilbert transform,and the interference is reduced. At last, the foundation of the fault state recognition andpredictive maintenance of gearbox were made. The research as follows:(1)The thesis introduces the wind generator fault diagnosis technology, the gearvibration fault characteristics, fault types and the analysis of the characteristics of severalkinds of fault vibration signal, signal processing.(2)Using a kind of gear fault diagnosis method based on Hilbert-Huang transformingmarginal spectrum analysis and achieving gear box single fault and composite faultdiagnosis. The intrinsic mode functions through empirical mode decomposition is obtainedusing the method of Hilbert transforming is got marginal spectrum, which analysis methodto realize gearbox fault judgments.(3)Fault characteristic frequency is extracted by collecting a wind field data and thenanalyzing the signal data by Matlab. Gearbox health states, main wear state, bearing outer punctuate corrosion condition and high speed shaft root crack state are classified andidentified.(4)The state of gear box is forecast by putting forward the BP neural network.According to the neural network algorithm model, the gear box fault type recognition isachieved. The results of the gear vibration signal in the Matlab platform processing showsthat the neural network system can realize gear box state trend prediction.