The vibration signals from rotating machinery are constantly mixed with other noises during the acquisition process, which has a negative impact on the accuracy of signal feature extraction. For vibration signals from rotating machinery, the conventional linear filtering-based denoising method is ineffective. To address this issue, this paper suggests an enhanced signal denoising method based on maximum overlap discrete wavelet packet transform (MODWPT) and variational mode decomposition (VMD). VMD decomposes the vibration signal of rotating machinery to produce a set of intrinsic mode functions (IMFs). By computing the composite weighted entropy (CWE), the phantom IMF component is then removed. In the end, the sensitive component is obtained by computing the value of the degree of difference (DID) after the high-frequency noise component has been decomposed through MODWPT. The denoised signal reconstructs the signal’s intrinsic characteristics as well as the denoised high-frequency IMF component. This technique was used to analyze the simulated and real-world signals of gear faults and it was compared to wavelet threshold denoising (WTD), empirical mode decomposition reconstruction denoising (EMD-RD), and ensemble empirical mode decomposition wavelet threshold denoising (EEMD-WTD). The outcomes demonstrate that this method can accurately extract the signal feature information while filtering out the noise components in the signal.

Rotor fault diagnosis has attracted much attention due to its difficulties such as non-stationarity of fault signals, difficulty in fault feature extraction and low diagnostic accuracy of small samples. In order to extract fault feature information of rotors more effectively and to improve fault diagnosis precision, this paper proposed a fault diagnosis method based on variational mode decomposition (VMD) symmetrical polar image and fuzzy neural network. Firstly, the original rotor vibration signal is decomposed by using the VMD method and the relevant parameter selection algorithm of the VMD method is also proposed. Secondly, the intrinsic mode functions (IMF), which are sensitive to the signal characteristics, are selected for signal reconstruction based on a comprehensive evaluation factor method. As well, the reconstructed signal is transformed into a two-dimensional snowflake image through using the symmetrical polar coordinate method. Finally, the image features are extracted by the gray level co-occurrence matrix to form the state feature vector, which is input into the fuzzy neural network to realize the rotor fault diagnosis. Through the analysis of measured signals, the experimental results show that the proposed method can reach a higher recognition rate of 98% and the k-cross-validation experiment is used to demonstrate the robustness of the fuzzy neural network, and the average recognition accuracy of this experiment is 99.2%. Compared with some similar methods, the proposed method still has the highest fault recognition precision 98.4%, and the smallest standard deviation 0.5477.

Conventionally, fused deposition modeling (FDM) 3D printing allows for multiple color printing, but it is limited to only various monochromatic colors. Consequently, the effect of progressive color transition cannot be reflected. To produce the progressive 3-D color printing effect, the only solution is to implement stereolithography technology, which is particularly expensive. Therefore, the aim of this paper is to develop a color mixing mechanism to be incorporated into an FDM 3D printer, which is relatively inexpensive. The underlying idea is to pre-mix the color so that the FDM 3D printer can produce a progressive color printing effect. Three conceptual color mixing mechanisms are designed, i.e., a triangular stirring shaft, a rectangular spoiler stirring shaft, and a spiral blade stirring shaft. The mixing process is modeled based on the non-Newtonian fluid theory, in which the Carreau model is used to simulate the motion of pseudoplastic fluids in FDM 3D printing under forced mixing. The resulting mixing ratio produced by all the designs is computed, which inspires the integrated design of rectangular spoiler stirring shaft and the spiral blade string shaft. Subsequently, the axial velocity of the mixed-color fluid, which increases from inlet to outlet, is verified. The integrated design is then fabricated and incorporated into the FDM 3D printer, and the progressive color printing effect is practically demonstrated.