A Data-driven Method for Identifying Intricate Trend Component Hidden in Measured Signal

Identification of the latent trend component is a vital procedure for further evaluating the measured signal. Many methods have been presented to extract the trend component. However, some essential parameters used in these methods need to be selected depending on much prior knowledge or experience. To avoid the inherent flaws in current methods, we introduce a novel signal decomposition method called variational mode decomposition (VMD) to cope with this issue. Firstly, the parameters involved in VMD method are discussed according to the universal characteristics of trend component. Then, a novel data-driven method based on the iterative VMD is proposed to identify the intricate trend component. Moreover, a criterion called normalized euclidean distance (NED) is presented to evaluate the converging property of the proposed method. Finally, the simulated time series and the collected extensive experimental cases are employed to illustrate the effectiveness of the proposed method. The analysis results show that the proposed method delivers a good performance for the trend component extraction and outperforms the benchmark method, i.e., empirical mode decomposition (EMD)-based method, for which more than one intrinsic mode function (IMF) is regarded as the underlying trend component.