To address the limitations of weak information extraction of rolling bearing fault features and the poor generalization performance of diagnostic methods, a novel method was proposed based on sparrow search algorithm (SSA)-Variational Mode Decomposition (VMD) and refined composite multi-scale dispersion entropy (RCMDE). Firstly, SSA optimized the key parameters of VMD to decompose the fault signal. The time-frequency domain comprehensive evaluation factor algorithm was then employed to select the sensitive intrinsic mode function (IMF) components for reconstruction. Then, RCMDE extracted features from the reconstructed signals to create a state feature set, which was input into the K-means KNN (KKNN) classifier for classification. To verify the effectiveness of the proposed method, comparative decomposition methods were established: EMD-RCMDE, EEMD-RCMDE, CEEMDAN-RCMDE, and RCMDE. Various feature extraction methods were also evaluated, including MDE, MFE, and MPE, along with classifiers such as DT, RF, and SVM. Experimental verification on different types of single and compound faults demonstrated the proposed method’s excellent fault identification capability. In order to further assess generalization ability and robustness, noise was artificially added to the single fault signals of the rolling element with varying damage levels. The results show that even under-noise interference, the proposed method maintained high fault identification accuracy excellent anti-noise performance and good generalization ability, which provides a certain reference for the solution of such problems.

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm’s convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm’s enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.

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.

Single-crystal sapphire specimen (α-Al₂O₃) have been widely applied in the semiconductor industry, microelectronics, and so on. In order to shorten the production time and improve the processing efficiency of sapphire processing, an integrated fixed-abrasive tool (FAT) based on solid-phase reactions is proposed in this article. The optimal FAT composition is determined using a preliminary experiment and orthogonal experiments. The mass fraction of the abrasives is chosen as 55 wt%, and the mass ratio of SiO₂/Cr₂O₃ is 2. Surface roughness R_a decreased from 580.4 ± 52.7 nm to 8.1 ± 0.7 nm after 150 min, and the average material removal rate was 14.3 ± 1.2 nm/min using the prepared FAT. Furthermore, FAT processing combined with chemical mechanical polishing (CMP) was shortened by 1.5 h compared to the traditional sapphire production process in obtaining undamaged sapphire surfaces with a roughness of R_a < 0.4 nm, which may have the potential to take the place of the fine lapping and rough polishing process.

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.

This study focuses on enhancing the intelligence level of assisted living robots to meet the needs of older adults with disabilities. The goal is to achieve efficient multitarget detection of everyday objects and to improve the accuracy and recall of the detection algorithm. To this end, an improved YOLOv8 algorithm (YOLOv8 - CBW3) is proposed. First, the GAM global attention mechanism is embedded in the backbone network of YOLOv8 to improve the sensitivity of the backbone network to the important feature information and reduce the attention to the irrelevant features, and it is determined that the GAM global attention mechanism will be embedded in the backbone network of YOLOv8 after comparing and analyzing the GAM with the attention mechanisms of CBAM, SE, and ECA; second, at the neck layer, the original PAN-FPN network structure is replaced with the BiFPN structure at the neck layer to realize bidirectional cross-scale connectivity and weighted feature fusion. In addition, a small-target detection layer is added to the original YOLOv8 model to enhance the detection capability for small-scale targets. Finally, the WIoUv3 loss is used instead of the CIoU loss as the localization regression loss function, which reduces the influence of low-quality anchor frames and enhances the model's focus on global information, thus improving the accuracy of target object detection. After 300 rounds of testing on 3000 data samples, the YOLOv8 - CBW3 algorithm achieves a detection accuracy of 95.11%, a recall of 94.60%, and an average precision of 95.23%, which are improvements of 2.78% and 3.72% in accuracy and recall, respectively, compared with YOLOv8n. A comparison with other popular models shows that YOLOv8 - CBW3 has strong generalizability, localization performance, detection ability and robustness. To verify that YOLOv8-CBW3 has strong generalizability, a comparative analysis is conducted on the VOC2007 public dataset, which shows through the data that its detection ability is still optimal, and it provides a reference solution for subsequent similar problems.