Abstract
Aiming at improving the accuracy of consumption prediction, a hybrid model was constructed, which designs an empirical wavelet filter bank to remove noise factors in original data. Besides the value prediction, the EWT-PGPR model can also give a certain credible interval, which effectively improves the practicability of the model.
Highlights
The noise factor can reduce the prediction accuracy deeply
The recovery of equipment operational capability is closely related to equipment maintenance support, and in which, bearing spare parts play an important role in equipment maintenance support
The accurate prediction of the consumption of bearing spare parts can meet the requirements of equipment maintenance support under the limited funds
Summary
The noise factor can reduce the prediction accuracy deeply. The recovery of equipment operational capability is closely related to equipment maintenance support, and in which, bearing spare parts play an important role in equipment maintenance support. The neural network grey theory [1] and support vector machine [2] and lots of other consumption forecasting methods were established. Without considering the noise caused by the special circumstances such as operator error, the prediction methods of the bearing spare parts consumption often analyzed directly by the original data. In order to improve the situation, the empirical wavelet transform (EWT) was established to denoise the consumption series, which makes up for the lack of adaptive data processing ability of wavelet transform [3, 4] and the need of selecting wavelet basis in advance. The prediction accuracy of non-stationary time series can be effectively improved by discarding the influence of noise in original data. Compared with the prediction methods such as neural network and support vector machine, the PGPR model has better adaptive parameter estimation and flexible nonparametric inference ability
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