1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
2. Military Transportation University, Tianjin 300161, China
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.
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2014, Vol. 54 
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