Wavelet de-noising method with threshold selection rules based on SNR evaluations

Acceleration signals from automotive automatic transmission bench tests need to be de-noised. Wavelet threshold de-noising requires a relatively small amount of computations and has better filtering. However, different wavelet basic functions and different threshold rules produce different noise signal de-noising results. Optimal matching parameters are found for simulated signals that approximate an observed signal through de-noising tests using the signal-to-noise ratio (SNR) and root-mean-square error (RMSE) of the de-noised signal as the evaluation index. The results are combined with the angular acceleration signal de-noise processing obtained in a bench test for the filtering. The wavelet transform de-noising is efficient, stable and not easily distorted when dealing with noise signals. The effect of the wavelet de-noising noisy signal can be evaluated using SNR combined with RMSE for the objective evaluation. Different signals may need to use different wavelet basis functions with different threshold rules.