Abstract
Research on damage diagnosis or safety monitoring based on structural vibration response is one of the hot issues in the engineering field. The characteristic information of the structure is obtained by analyzing the structure response data. In the process of data analysis, the choice of data length is very important, which is related to the validity of the structure monitoring results. At present, the selection of data length is usually subjective, which reduces the rigor of the structure monitoring process. Therefore, a method based on improved multiscale permutation entropy (IMPE) is proposed to determine the optimal data analytical length (ODAL) of vibration data. This method creatively applies multiscale permutation entropy (MPE) to the field of data length analysis when processing nonlinear and nonstationary signals and optimizes MPE with the help of the improved coarse-grained method to obtain IMPE. IMPE is sensitive to different data lengths, and the entropy changes with the increase of the data length and tends to be stable. Here, the stable value is defined as a standard entropy. The entropy satisfying 97% of the standard entropy is used as the effective entropy, and the corresponding data length value of the effective entropy is selected as the ODAL of the vibration data. This method is suitable for many fields, provides a reliable data analytical length for data analysis, and has good engineering practicability.
Highlights
Erefore, a method based on improved multiscale permutation entropy (IMPE) is proposed to determine the optimal data analytical length (ODAL) of vibration data. is method creatively applies multiscale permutation entropy (MPE) to the field of data length analysis when processing nonlinear and nonstationary signals and optimizes MPE with the help of the improved coarse-grained method to obtain IMPE
In this paper, based on the identification of vibration data feature information by IMPE, we explore the entropy change rules under different data lengths to find the ODAL with complete feature information and high computing efficiency. e research results show that this method can select the ODAL for safety monitoring of hydraulic structures, improve the efficiency of vibration data analysis, and provide a reference basis for setting the length of data analysis for similar projects
Combined with the superiority of multiscale permutation in nonlinear signal mutation detection, a method to determine the ODAL of vibration data based on improved multiscale permutation entropy (IMPE) is proposed. e method is applied to the optimal data analytical length selection of white noise simulation signals and actual engineering structural vibration monitoring data. e main conclusions are as follows: (1) By analyzing the simulated signal with different SNR, it is proved that the improved multiscale permutation entropy has strong antinoise ability and good robustness, which can effectively avoid the influence of mixed noise on the accuracy of calculation results
Summary
The principles of IMPE and the method on selecting the ODAL are presented. In order to solve this problem, HumeauHeurtier et al [20] proposed the coarse-graining process based on the moving average and applied it to the calculation of sample entropy In this way, the optimized coarsegraining method was applied to the calculation of MPE to improve the accuracy of results. As an important step before entropy value calculation, the selection of phase space reconstruction parameters is closely related to the accuracy of signal analysis results, which can be roughly divided into independent determination and joint determination methods. E determination of ODAL based on IMPE uses the sensitivity of the entropy value to the sudden change of dynamical system to find the suitable length of data, so as to solve the problem of data length selectivity in data analysis It can be divided into the following four parts: coarse-graining, parameter selection and phase space reconstruction, calculation of entropy value, and selection of sequence length. (6) Compare MPE (N1), MPE (N2), . . ., MPE (Ni), . . ., MPE (Nn) with MPE (Nn), respectively, and select MPE (Ni) which is greater than 97% of the MPE (Nn), and the shortest data length corresponding to MPE (Ni) is defined as the ODAL of the vibration data
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