Multi-sensor intelligent detection method based on uncertainty reasoning

A variety of uncertain problems are caused by the complexity of multi-sensor detection environment, the limitations of sensors and the imperfection of information acquisition, etc. The information is presented to random, low signal to noise ratio and incomplete. A multi-sensor distributed fusion intelligent detection method based on uncertainty reasoning is proposed for the uncertainty characteristics of nonlinearity, non-stationarity and the poor on the information. The method is based on the Subjective Bayes reasoning, and the local decision rules acquisition model is built. Finally, the global decision is generated. The experiment shows that the reliability of fault identification has been improved by the method, and compared with the traditional method, the above method has the advantages of high recognition rate, fast diagnosis speed, etc., which will provide reliable sample data for multi-information fusion intelligent fault diagnosis. © ICIC International 2015.