Application of VMD in noise removal of TDLAS methane gas detection

In recent years, tunable diode laser absorption spectroscopy has become one of the important technologies in the field of gas detection owing to its high sensitivity and fast response. However, substantial noise is often present in actual measurements, affecting the detection accuracy. In this paper, empirical mode decomposition, variational mode decomposition, wavelet decomposition and Kalman filtering are used to denoise the measured signals. The noise reduction performance of different methods is evaluated by selecting five indicators: root mean square error, mean absolute error, mean absolute percentage error, coefficient of determination, and signal-to-noise ratio. Experimental results show that under four different concentration conditions, variational mode decomposition achieves the best overall performance compared with the other three methods: the root mean square error, mean absolute error, and mean absolute percentage error are reduced by an average of 69.42%, the coefficient of determination is increased by 38.65%, and the signal-to-noise ratio is improved by 53.09%.