Research On Rolling Bearing AE Signal Feature Selection And State Recognition Method

This project comes from the Nation Natural Science Funds project theresearch of mechanical fault wireless sensor network monitoring andintelligent diagnosis method, project number:51075023. Acoustic emissiontesting technology for fault diagnosis field is one of the hot research issues.Because it is in high frequency range, is hardly disturbed by vibration noisein low frequency. It has a good application prospect. But when the acousticemission test technology is applied in industrial production, it has theproblems: how to get effectively signal-to–noise separation, how to selectthe characteristic parameters in order to improve the accuracy of staterecognition, how to select the suitable recognition method for differentrequirements.The project research and design a complete set of rolling bearingacoustic emission monitor, which is based on the hardware design ofacoustic emission wireless monitor unit. This paper proposes the signalpretreatment method, which combines the forced de-noising method andwavelet threshold de-noising method based on wavelet transform; and the entropy introduced into selection wavelet function, it can help choosing thewavelet function fit for acoustic emission signal of rolling bearing. Thismethod can effectively separated signal-to-noise. This paper proposes thenew feature selection method based on combine mutual information theoryand distance measurement. The characteristic parameter subset based on thenew method can get higher recognition accuracy compared with thetraditional method. The fuzzy recognition is improved by the coefficients,which is the relationship between the parameters and state, also the distancemeasurement between parameters. The improved fuzzy recognition methodgets good recognition result. The envelope spectrum method and improvedfuzzy are used in recognitionsimple recognition of monitor unit, and the antcolony clustering recognition and particles swarm clustering recognitionalgorithm are used in accurate recognition of PC. The model combinessimple recognition of monitoring unit with accurately recognition of PC,improving the efficiency of the rolling bearing state recognition.