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    • Feature Extraction and State Recognition for Sliding Bearing Friction Faults Based on Variational Mode Decomposition
    DOI：
    Key Words:IC engine  diesel engine  sliding bearing  variational mode decomposition  feature extraction  state recognition
    Author NameAffiliation
    ZHANG Yunqiang，ZHANG Peilin，WANG Huaiguang，YANG Yudong 1.Department of Vehicle and Electrical Engineering，Ordnance Engineering College，Shijiazhuang 050003，China
    2.The 4th Department，Wuhan Mechanical Technology College，Wuhan 430075，China 
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    Abstract:In view of the sliding bearing nonlinear and non-stationary vibration signals and its weak friction signals， a method of feature extraction and state recognition method for sliding bearing friction faults based on variational mode decomposition（VMD） was proposed. The VMD was used to adaptively decompose the sliding bearing vibration signals into the system shock signal， low frequency friction signal and high frequency friction signal. On the basis of VMD decomposition， the feature parameters of relative frequency spectrum energy moments were defined and extracted， which were used to describe the characteristics of sliding bearing vibration signals and their components. The crankshaft bearing friction fault signals from a S195-2 diesel engine were analyzed， and the average recognition accuracy of K-nearest neighbor classifier reached 93.3%. Results indicate that the relative frequency spectrum energy moments extracted on the basis of VMD are sensitive the working conditions of sliding bearing and can effectively identify its friction fault states.
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