Denoising and Feature Extraction for Space Infrared Dim Target Recognition Utilizing Optimal VMD and Dual-Band Thermometry

An Optimized VMD Method for Predicting Milling Cutter Wear Using Vibration Signal

Tool wear has a negative impact on machining quality and efficiency. As for the nonlinear and non-stationary characteristics of vibration signals and strong background noises during the milling process, an identification method of the milling cutter wear state based on the optimized Variational Mode Decomposition (VMD) was proposed, in which the objective function is to minimize the Envelope Entropy (Ep); the various modes of the vibration signal are decomposed using the self-adaptive optimization parameters with Differential Evolution (DE). According to the cross-correlation coefficient in the frequency domain between Intrinsic Mode Function (IMF) and the original signals, the informative IMF components were selected as the sensitive IMF components to superimpose the reconstruction signal and extract the eigenvalues. The mapping relationship between the eigenvalues and the milling cutter wear degree is established by the Naive Bayes classifier method. The experimental results under the various operation conditions indicate that the proposed optimized VMD method possesses an excellent generalization performance. Compared with Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD), it has better denoising capacity, and so can improve the identification accuracy of the milling cutter wear. Therefore, the processing quality and production efficiency are ensured effectively.

A Denoising Method of Micro-Turbine Acoustic Pressure Signal Based on CEEMDAN and Improved Variable Step-Size NLMS Algorithm

The acoustic pressure signal generated by blades is one of the key indicators for condition monitoring and fault diagnosis in the field of turbines. Generally, the working conditions of the turbine are harsh, resulting in a large amount of interference and noise in the measured acoustic pressure signal. Therefore, denoising the acoustic pressure signal is the basis of the subsequent research. In this paper, a denoising method of micro-turbine acoustic pressure signal based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variable step-size Normalized Least Mean Square (VSS-NLMS) algorithms is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original signal into multiple intrinsic mode functions (IMFs), based on the cross-correlation coefficient and continuous mean square error (CMSE) criterion; the obtained IMFs are divided into clear IMFs, noise-dominated IMFs, and noise IMFs. Finally, the improved VSS-NLMS algorithm is adopted to denoise the noise-dominated IMFs and combined with the clear IMF for reconstruction to obtain the final denoised signal. Adopting the above principles, the acoustic pressure signals generated by a micro-turbine with different rotation speeds and different states (normal turbine and fractured turbine) are denoised, respectively, and the results are compared with the axial flow fan test (ideal interference-free signal). The results show that the denoising method proposed in this paper has a good denoising effect, and the denoised signal is smooth and the important features are well preserved, which is conducive to the extraction of acoustic pressure signal characteristics.