An improved denoising algorithm based on wavelet transform modulus maxima for non-intrusive measurement signals

An improved denoising algorithm based on wavelet transform modulus maxima (WTMM) is proposed for processing non-intrusive measurement signals in pneumatic systems. Combined with the adaptive BayesShrink threshold and Witkin's scale space tracking theory, the process of tracing the evolution of WTMM over scales has been optimized. Thus, the signal and the noise can be identified and isolated effectively. Additionally, to improve the computational efficiency, a fast method based on a piecewise cubic Hermite interpolating polynomial is applied to reconstruct the signal. Numerical experiments confirm the advantages of the improved denoising algorithm: compared with the classical algorithm of WTMM, the improved denoising algorithm not only increases the signal to noise ratio by over 10% but also reduces the processing time by over 90%. More generally, the algorithm has better performance than that of several typical algorithms in its denoising quality, adaptability and practicality.