Publication Date:
2018-08-11
Description:
Sensors, Vol. 18, Pages 2621: Future Trend Forecast by Empirical Wavelet Transform and Autoregressive Moving Average Sensors doi: 10.3390/s18082621 Authors: Qiusheng Wang Haipeng Li Jinyong Lin Chunxia Zhang In engineering and technical fields, a large number of sensors are applied to monitor a complex system. A special class of signals are often captured by those sensors. Although they often have indirect or indistinct relationships among them, they simultaneously reflect the operating states of the whole system. Using these signals, the field engineers can evaluate the operational states, even predict future behaviors of the monitored system. A novel method of future operational trend forecast of a complex system is proposed in this paper. It is based on empirical wavelet transform (EWT) and autoregressive moving average (ARMA) techniques. Firstly, empirical wavelet transform is used to extract the significant mode from each recorded signal, which reflects one aspect of the operating system. Secondly, the system states are represented by the indicator function which are obtained from those normalized and weighted significant modes. Finally, the future trend is forecast by the parametric model of ARMA. The effectiveness and practicality of the proposed method are verified by a set of numerical experiments.
Electronic ISSN:
1424-8220
Topics:
Chemistry and Pharmacology
,
Electrical Engineering, Measurement and Control Technology
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