A novel method for the recovery of continuous missing data using multivariate variational mode decomposition and fully convolutional networks

Abstract

The sensor failure is the frequently-occurring event in engineering activities related to the measurement, which could yield a serious problem of continuous data loss. This special issue will affect the accuracy of structural damage identification and condition assessment. However, traditional methods (e.g., deep learning methods) are hard to settle the recovery of the data with complex characteristics. To this end, this study develops an innovative framework based on the combination of multivariate variational mode decomposition (MVMD), fully convolutional networks (FCN), and a systematic evaluation pattern. In this framework, MVMD is first used to simultaneously decompose the multichannel data into a set of intrinsic mode functions (IMFs) with a modal alignment attribute. Then, FCN is designed to construct a mapping relationship among different IMFs having the same level number in an end-to-end manner, by which the continuous data recovery at the location of interest can be realized. Obviously, this method can not only make the utmost of the spatial information, but also involve an excellent data mining ability of FCN. Further, a systematic pattern from the perspective of time and frequency domains is defined to evaluate the recovery ability of the concerned models. Finally, case study based on the measured data of a steel-frame reinforced arch rib is used to exhibit the performance of the proposed method. Additionally, the parameter analysis and practical application are respectively performed to examine its robustness and practicality. All these results demonstrate the superiority of this method, and its performance is closely related to the location of interest, the number of training data channels and their corresponding location.