A coarse-to-fine denoising method for dynamic calibration signals of pressure sensor based on adaptive mode decompositions. (15th October 2020)

• Record Type: Journal Article
• Authors: Yao, Zhenjian; Liu, Xiaojun; Yang, Wenjun; Wang, Chenchen; Chang, Suping
• Is Part Of: Measurement. Volume 163(2020)
• Publication Date: 2020-10-15
• Subjects: Pressure sensor -- Dynamic calibration -- Signal denoising -- Variational mode decomposition -- Empirical mode decomposition -- Ringing characteristic; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.107935
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A coarse-to-fine denoising method is proposed for improving the calibration signal quality. An adaptive method is proposed for estimating the optimal mode number of VMD. Two indicators are introduced to identify the relevant modes of decomposition results. Abstract: Dynamic calibration is an essential way to characterize the measurement performance of pressure sensors under dynamic environment. However, it is difficult to guarantee the reliability of calibration results because the calibration signal is inevitably contaminated by noises. In this paper, a coarse-to-fine denoising method is proposed to improve the quality of calibration signals based on adaptive mode decompositions. Firstly, variational mode decomposition (VMD) is used to decompose the calibration signal into several band-limited intrinsic mode functions (BLIMFs). The optimal mode number is estimated based on their center frequency spacing and mutual information of BLIMFs. The coarsely denoised signal is then reconstructed by the relevant BLIMFs based on the ringing energy loss ratio indicator. Subsequently, the coarsely denoised signal is decomposed as a series of IMFs with empirical mode decomposition (EMD). By introducing a clustering indicator named as comprehensive weighted correlative degree, the ringing and trend IMFs are extracted for obtaining the finally denoised result. The performance of the proposed method is validated by both simulated and actual dynamic calibration signals. Results show that the SNR of denoised result with the proposed method is 33.91, which is obviously larger than that obtained by EMD (SNR = 25.03) and VMD (SNR = 22.67) for simulated signal. Furthermore, comparative experiments also demonstrate the superiority of the proposed method over the existing approaches in both denoising ability and signal integrity.