Prognostics for Rotating Machinery Using Variational Mode Decomposition and Long Short-Term Memory Network
| Autor: | Rong Yao, Jiahe Niu, Linxuan Zhang, Chongdang Liu, Cheng Wu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Bearing (mechanical) Computer science Reliability (computer networking) 020208 electrical & electronic engineering Feature extraction Local regression 02 engineering and technology Filter (signal processing) computer.software_genre law.invention 020901 industrial engineering & automation Feature (computer vision) law 0202 electrical engineering electronic engineering information engineering Prognostics Data mining computer Smoothing |
| Zdroj: | SMC |
| DOI: | 10.1109/smc.2019.8913840 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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