Prognostics for Rotating Machinery Using Variational Mode Decomposition and Long Short-Term Memory Network
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)(2019)
摘要
Rotating machinery prognostics plays an important role in promoting reliability and efficiency in the operation of machinery and reducing maintenance costs. This paper proposes a novel bearing remaining useful life prediction approach which puts emphasis on deriving effective features from raw vibration data. To enhance the degradation feature extraction, a non-recursive method named variational mode decomposition (VMD) is adopted to decompose the raw vibration data into several principal modes, then feature smoothing with a local regression filter is performed and some suitable features are selected by evaluating feature fitness using monotonicity and correlation analysis. Based on the selected features, long short-term memory (LSTM) network is introduced for bearings RUL prediction. Numerical experiments with real bearing dataset exhibit the effectiveness and superiority of the proposed approach in comparison to other data-driven approaches.
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关键词
Remaining useful life,variational mode decomposition,feature selection,Long Short-Term Memory
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