Abstract
Since the equipment early fault signal is very weak and unstable, it is very difficult to judge the fault type and fault degree through the traditional method of fault diagnosis .For this reason, this paper presents an effective method. Firstly, the fault signal is decomposed into a number of intrinsic mode functions (IMF) by Empirical Mode Decomposition (EMD) method, then the IMF’s energy entropy is calculated, the AR model is established, and the auto-regressive parameters are extracted. At the same time, the residual variance of the model is taken as the fault feature vector, which can be used as input parameters of SVM classifier to distinguish working state and fault types of the equipment. Finally, the validity of the proposed method is verified by taking the rolling bearing fault diagnosis as an example.
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.
