The Adaptive De-Noising Based on Dual-Tree Complex Wavelet Transform for Ultrasonic Echo Signal of Wear Debris in Lubricant Oil

In order to reduce the noise containing in ultrasonic echo signal of wear debris in lubrication, an adaptive method was proposed based on dual-tree complex wavelet transform (DT-CWT) to extract clear and accurate echo signals. Combining singular spectrum analysis (SSA) and wavelet entropy theory, the approximate and detail section of dual-tree complex wavelet transform were analyzed respectively. Singular spectrum analysis was used to remove the noise contained in the approximate section, and the wavelet entropy theory was applied to select thresholds adaptively in different decomposition levels, to achieve the adaptive choosing of detail coefficients. Simulation results show that after the adaptive de-noising based on dual-tree complex wavelet transform, the output signals have higher signal-to-noise ratio (SNR), smaller root mean square error (RMSE), higher normalized correlation coefficient (NCC) and the runtime of algorithm meets the requirement of online detection application. The experimental results show that this method can efficiently reduce the noise of ultrasonic echo signal and restore the accurate wave shape features.