Research On Fault Diagnosis For Wind Turbines Rolling Bearing Based On Full Vector Spectrum

Hilbert demodulation and HHT both are effective methods to process the nonlinear and non-stationary signals and are able to recognize the fault characteristics effectively, which have been applied into the fault diagnosis for wind turbines rolling bearings successfully. However, the validity and reliability of the traditional diagnosis based on one-chanel vibration signal is reduced, because one-chanel vibration signal can not fully refect the actual running state of the rolling bearings in fact.To improve this situation, two new methods to diagnose rolling bearing faults were put forward, namely Full Vector Hilbert demodulation and Full Vector HHT marginal time spectrum. The main research is as follows:This paper introduces the common failure styles, the failure mechanism, the vibration characteristic of rolling bearings and the theory of Full Vector Spectrum,Hilbert demodulation and HHT briefly. The numerical algorithms of Full Vector Spectrum are expounded and proved that vector spectrum can be totally compatible to the signals at the same cross section and accurately describe the actual state of the rotors. And it overcomes the differences in spectrum structures coming from different directions at the same cross section of general frequency spectrum analysis, then the unique spectrum structure is obtained, which would provide a reliable basis to the next feature extraction and fault diagnosis.The whole fatigue lifecycle tests are conducted in the bearing test machine and the typical faults experiments are conducted in the WTDS test-beds. By combining the Full Vector Spectrum with Hilbert demodulation and HHT, Full Vector Hilbert demodulation and Full Vector HHT marginal time spectrum are put forward. The methods were explained in theory firstly, and then simulations and experiments were conducted. The validity and reliability of the methods are poved in the fault diagnosis for wind turbines rolling bearings.