School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Beijing Aerospace Automatic Control Institute, Beijing 100854, China.
Sensors (Basel). 2018 Aug 10;18(8):2621. doi: 10.3390/s18082621.
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.
在工程技术领域，大量的传感器被应用于监测复杂系统。这些传感器常常捕获到一类特殊的信号。虽然这些信号之间通常存在间接的或不明显的关系，但它们同时反映了整个系统的运行状态。通过使用这些信号，现场工程师可以评估系统的运行状态，甚至可以预测被监测系统的未来行为。本文提出了一种基于经验模态分解（EMD）和自回归滑动平均（ARMA）技术的复杂系统未来运行趋势预测的新方法。该方法首先使用经验模态分解从每个记录信号中提取反映系统运行某一方面的显著模态，其次通过对这些标准化和加权的显著模态的指示函数来表示系统状态，最后使用 ARMA 的参数模型来预测未来趋势。通过一组数值实验验证了该方法的有效性和实用性。
