EWT-ASG: Empirical Wavelet Transform With Adaptive Savitzky–Golay Filtering for TDLAS

Abstract

Inspired by the empirical mode decomposition (EMD)-enhanced gas detection work, this paper develops a further improved signal reconstruction method (namely EWT-ASG) for the demodulated harmonics of tunable diode laser absorption spectroscopy (TDLAS), which is mainly based on empirical wavelet transform (EWT) and Savitzky-Golay (S-G) filtering. First, the imported EWT performs better on the decomposition precision as it successfully bypasses the mode aliasing problem of EMD resulting by the lack of mathematical basis. Second, the improved S-G filter effectively suppresses the noisy components of the wavelet coefficients by updating its one key parameter (i.e., window size w) dynamically according to the correlation coefficients between the raw signals and the decomposed wavelet coefficients. The EWT-ASG scheme was first applied on the oxygen concentration detection for pharmaceutical glass vials. The preliminary experimental results indicate that the EWT-ASG method performs better than recent state-of-the-arts, with an average correct discrimination rate of 98.14% when the normalized SNR is 1. Even when the normalized SNR is degenerated from 1 to 0.85, our detection system still survived well, with a highest average correct discrimination rate of 90.45%. The detection system precision is also improved to a large extent, with a minimal Allan deviation of 0.856@(117s).

Dynamic sparse residual oxygen prediction method for encapsulated pharmaceutical vials under TDLAS/WMS framework

“…Since then, a series of studies, such as ensemble empirical mode decomposition (EEMD) 17 and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) 18 , were proposed, but none of them could fundamentally make up for the lack of mathematical basis and theoretical support. He et al proved that empirical wavelet transform (EWT) outdoes EMD for the multi-resolution characteristics and flexibility to signals 19 , and the wavelet decomposition applied in an industrial scene 20 has been confirmed superior to deal with timevarying noises. Previously, Song et al proposed a noise-robust self-adaptive support vector machine (NSSVM), especially for the case of fast and slow time-varying noise interleaving 21 .…”

Performance analysis of moving average filtering used in TDLAS carbon dioxide sensing system

“…Feng Shiling et al [4] processed direct absorption spectra and second harmonic signals with different concentrations using wavelet transform noise reduction techniques and obtained a signal-to-noise ratio increase of more than 500 times, which proved the value of using wavelet transform in this field. Zou Debao et al [5] processed the TDLAS ammonia absorption signal using a variety of different filtering methods, and the results showed that the wavelet transform plus arithmetic averaging method was able to increase the signal-to-noise ratio by a factor of 14. Zhang Shuai et al digitally filtered the concentration signal by modified weighted moving average filtering, which improved the signal-to-noise ratio and the detection sensitivity of the system, and was applied to the real-time hydrogen sulfide detection in a natural gas processing plant.…”

Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix

“…At the same time, because EWT is not decomposed in an iterative way, the decomposition speed is very fast. Due to the above advantages, EWT has been widely used in the identification of the fault information of rolling bearings [ 20 , 21 ] and fan bearings [ 22 ]. In this paper, EWT is introduced into the processing of the foundation vibration signals and is used to perform the multi-scale decomposition of foundation vibration signals.…”