Abstract
Aiming at the uncertainty of fault signal and strong nonlinear trait of fault features,A Temporal-Spatial information fusion method of fault diagnosis is presented based on clustering means centralization. Extracting multiple template models from different types of fault,the means of which is regarded as the base point of certain clusters. Calculating the Euclidean distance between the fault features to be detected from different sensors and base point of all clusters,the reciprocal value of which is normalized to be Basic Probability Assignment (BPA) of every fault. With a period of continuous observations, multiples of BPA are combined with Dempster rule in every group of sensors in the time domain,then the results of every group combine again to get the final diagnostic results. Finally the case of motor rotor shows the effectiveness of proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
