On the basis of the information entropy distance of a coustic emission signals,an information entropy average distance diagnosis method for acoustic emission signals is proposed.This method can effectively improve the diagnostic accuracy of lubrication status of sliding bearings on steam turbines.By highlighting the information entropy characteristics of lubrication status and changing the distance algorithm between information entropy points,the differences between different lubrication statuses are more obvious to enhance the accurate recognition ability of lubrication status.This effect is most significant in the diagnosis of semi-dry friction state,and its effectiveness has been verified on actual units,providing a more reliable method for the diagnosis and fault prediction of sliding bearing lubrication state.
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