The methods of establishing of aero-engine vibration sensor network sensitive factor based on multi-feature fusion and transformation

摘要

There is no concept and indicator of vibration sensor network sensitive factors suggested in research reports,which makes it not possible to assess the degree of importance of each node of the sensor and to solve the consistency of decision-making fusion of multi-sensor.We propose two methods of sensor networks sensitive factor calculation: multi-feature fusion method and KPCA weighted nonlinear feature transformation method,both of which are based on the calculation of time and frequency domain feature sensitivity of the vibration signal.In the first method,it is obtained by weighted fusion of the most sensitive feature of each sensor.In the second method,it is obtained by nonlinear feature transformation of all features.It is verified by the rotor fault simulation data obtained by multi-sensor.The results showed that: the sensitive factor obtained in the two method can both reflect changes in failure or abnormal state,but KPCA feature transform method works better,because for the same fault or abnormal condition it has higher sensitivity factor and stronger sensitivity; sensitive factors obtained through two methods can both effectively measure the sensitivity of different sensor nodes for the same fault or abnormal condition in the network.The resulting sequencing of the sensitivity of sensors are consistent,and can both be used to calculate the degree of importance of the sensor nodes.