No abstract

Data from sensor array are often arranged in three-dimension as sample × time × sensor. Traditional methods are mainly used for two-dimension data. When such methods are applied, some time-profile information will lost. To acquire the information of samples, sensors and times more exactly, parallel factor analysis (PARAFAC) is investigated to deal with three-way data array. Through the analysis and classification of three kinds of oil odor samples, the performance of PARAFAC in gas sensor array signal analysis is verified and validated.

A method to analyze and detect the features of rotor and stator asymmetric faults for AC motor is put forward in this paper. After modeling the damaged asynchronous motor, stator’s three-phase current signals of the motor at normal status, as well as that with stator and rotor bar asymmetric faults, are simulated and analyzed qualitatively. In view of the shortcomings of Park’s vector transformation in the analyzing motor current signature, a method is put forward by combining it with signal amplitude demodulation. Based on this new method, the asymmetric features are expected to be extracted completely through the amplitude relative normalization and spectrum analysis. Finally, the availability of this method is verified by detecting and diagnosing the faults in actual AC motors.