Study on Noise Reduction of Tunable Diode Laser Absorption Spectroscopy Detection Signal Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Detrended Fluctuation Analysis-Wavelet Soft Threshold

Abstract

Tunable diode laser absorption spectroscopy (TDLAS) is a significant technique for measuring gas concentration in the combustion field. When the gas concentration is detected by wavelength modulation spectroscopy, the second harmonic (2f) signal demodulated by a lock-in amplifier can be analyzed to obtain the gas concentration information. However, the 2f signal will be affected by the white Gaussian noise of electronic equipment and the optical fringe of the standard instrument, which will lead to the reduction of the gas detection accuracy. To solve the above problems, this paper proposes a 2f signal noise reduction algorithm based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), detrended fluctuation analysis (DFA), and wavelet soft threshold (WST). Taking CO2 gas as an example, the 2f signal extracted from the experiment was denoised, and the amplitude of the 2f signal was linearly fitted to the gas concentration. The R2 value was 0.9979, and the SNR was 31.9750 dB. The denoising effect is obvious, and the denoising algorithm can better retain the peak information of the second harmonic signal. In this paper, the existing classical noise reduction algorithm is simulated and analyzed. To display the noise reduction effect visually in the time-frequency domain, the Hilbert-Huang transform three-dimensional spectrum is introduced to analyze the spectrum characteristics of the noise reduction signal.