Notice of RetractionGaussian Research of Turbine Faults Diagnosis Base on Mixture Models

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

The Gaussian Mixture Models and the wavelet packet analysis is used to the turbine vibration faults diagnosis. Decompounded firstly the vibration faults signal and delete the disturbed component. Then, distill the frequency segment that includes the faults character which seen as the faults characteristic vector. Set up the Gaussian Mixture Models with the vectors, and identify different faults with the built model. It used the experimentation data that measured in Benlty experiment table, to set up the model and identify faults. From the result, when the modulus equal to twelve , the faults diagnosis right rate of the Gaussian Mixture Models equal to approximately 80%~90%. It indicates that the means can acquire a good effect, which uses the Gaussian Mixture Models and the wavelet packet analysis to diagnose the turbine libration fault.