Abstract:This paper addresses the challenges of poor accuracy and reliability in fault diagnosis for hydraulic mechanical drive gear sets by proposing a research approach based on Support Vector Machines (SVM). The study begins by collecting vibration signals from the hydraulic mechanical drive gear group and constructing a fault signal separation model. Utilizing a low-rank algorithm, the research separates the vibration source signals of the hydraulic mechanical drive gearbox. Constraint conditions are designed for gear group fault signals to facilitate their classification. Based on these classification results, the SDAE model is employed to extract fault features from the hydraulic mechanical drive gear group. The extracted features are then input into the SVM for training, with the final output being the optimal diagnostic result. This approach achieves fault diagnosis of the hydraulic mechanical drive gear group based on SVM. Experimental results demonstrate that the classification error rate of this method does not exceed 3.5%, confirming its high feasibility.

Abstract:To address the limitations of conventional pulsed remote field eddy current testing (PRFECT) in accurately determining the circumferential position of defects, we propose an innovative array-based PRFECT probe. This method involves increasing the number of receiving coils and modifying the relative positions of the receiving and excitation coils to enhance defect localization capabilities. To address the issue of weak signals detected, a hybrid signal denoising technique combining successive variational mode decomposition (SVMD) and singular value decomposition (SVD) is introduced. Initially, the signal is decomposed into a series of modal functions using SVMD. Subsequently, components for reconstruction are selected based on the Pearson correlation coefficient. The retained components are then denoised using the SVD method, and these denoised components are superimposed to reconstruct the signal. Both simulations and experimental results demonstrate that the proposed novel probe effectively locates defect positions, The proposed algorithm can improve the Denoising Signal-to-Noise Ratio of the measured key signal to 9.30, better than traditional algorithm.

Abstract:In order to improve the accuracy of pipe percussion detection in the wall, this paper uses the fine composite multi-scale dispersion entropy to detect the frequency and amplitude changes of the percussion sound signal, and extract the multi-scale pipe features in the signal. The multi-dimensional pipeline feature matrix was input into the support vector machine, and the sparrow search algorithm was used to determine the optimal value of the parameters of the support vector machine. The classification of buried pipelines in the wall was completed through model training, and a knocking detection method of pipelines in the wall based on fine composite multi-scale dispersion entropy was proposed. Comparing this method with other signal processing methods, the results show that the detection accuracy of the proposed method is up to 97%, which is much higher than the other two methods.

Abstract:In the Industrial Internet of Things, the massive data generated by the SCADA of wind turbines is not suitable for being directly sent to the cloud for processing due to real-time requirements. This paper designs and build a set of Micro-Wind Turbine equipment condition monitoring system based on Edge Intelligence.Three unsupervised anomaly detection algorithms, including OC-SVM, IForest and HBOS, are analyzed and compared with each other. The experimental results show that OC-SVM have the best real-time anomaly detection effect. The F1 scores in the rotation anomaly test set and vibration anomaly test set are 0.997 and 0.969，respectively. This paper can provide some reference value for the landing verification of edge side training and reasoning scheme.

Abstract:Aiming at the problem of feature extraction and fault diagnosis of rolling bearing vibration signals, a fault diagnosis method of rolling bearing based on wavelet packet information entropy and support vector machine (SVM) optimized by snake optimization algorithm (SO) is proposed. The collected vibration signals are processed by using the wavelet packet, the energy spectrum entropy and the coefficient entropy of the wavelet packet are constructed, and the constructed feature vectors are input into the SO-SVM for identification and classification; Finally, the multi-fault pattern recognition is realized and the diagnosis results are output. The simulation results show that the diagnostic accuracy of this method for five different groups of samples reaches 99.17%~100%, and compared with FOA-SVM and PSO-SVM, it has a higher effect of fault recognition and classification.

Abstract:Wireless network control is a favorable factor to promote the intelligence of high-speed trains. As a typical time series, wireless network delay has strong randomness, large volatility and other problems leading to difficult prediction. In view of these problems, a wireless network delay prediction model with singular spectrum analysis-improved particle swarm optimization and LSSVM is proposed. The length of the window was first determined by the Cao method, the delay sequences were analyzed by singular spectral analysis to obtain a series of subsequences. Each subsequence was predicted using the LSSVM model optimized for the chaotic particle swarm. Finally, all the subsequence predicted values were superimposed to obtain the final prediction results, the simulation results show that the average absolute percentage error (MAPE), mean squared error (MSE) and average absolute error (MAE) are 2.8%, 1.055 and 0.44 lower respectively compared with the wavelet decomposition model. Compared with the EMD decomposition model, 7.4%, 3.377 and 1.118 decreased, respectively. Compared with the CEEMD decomposition model, it was reduced by 6.2%, 2.568, and 0.974, respectively. The accuracy was significantly higher than that in the other models.

Abstract:In order to optimize the extraction of pearl color features and improve the accuracy of pearl color detection, a shadow detection algorithm based on K-means clustering and local gradients is proposed. The results show that the algorithm can accurately detect the shadow of pearls in pearl images and eliminate the interference of shadows on the extraction of color features. In the Lab color space, a color feature extraction based on the area of pearl echo gallery effect is proposed, GA-SVM is used as the pearl color identification method, and a secondary color detection strategy is proposed to determine the pearl color category through two color detections. The comparison experimental results show that the accuracy rate of pearl body color detection is 100%, and the accuracy rate of pearl color detection is 98.7878%.

Abstract:An automatic tagging robot needs to be provided the 3D coordinates of a bar’s bottom center to weld a tag on the bundle. A method based on binocular vision was proposed to select and localize bars’ bottom centers. Virtual image camera model was adopted in the binocular vision system. The extrinsic parameters of two cameras were calibrated from a planar calibration pattern which was put parallel to the common end plane of bars. The virtual images of two cameras were created according to the calibrated results. A method using SVM and connected region was adopt to extract the center point features of bars in both virtual images from two cameras. A group of candidate features pairs were selected using epipolar constraint to the features and coplanar constraint to the recovered physical points. The 3D coordinates of corresponding physical points were recommended to the robot to try to weld the tag. Simulation results showed all recommended points to the robot were from correct matched pairs. It demonstrated effectiveness of the features matching method presented. In real experiment, the maximum depth displacement error of the recommended points was 0.20 mm, the average error was 0.09 mm. It demonstrated the effectiveness of bar bottom center extraction method presented.

Abstract:In the process of oil and gas pipeline leak detection, the leak signal contains a lot of noise and the feature extraction is difficult. An improved total average empirical mode decomposition combined with Kalman filter algorithm is proposed to denoise the pipeline signal. First, the improved overall average empirical mode algorithm is used to decompose the collected pipeline negative pressure wave signal. The permutation entropy and Kalman filter algorithm are used to filter and process the decomposed inherent modal components, and finally the reconstructed Cut the noise signal. Furthermore, a feature extraction method based on diffusion entropy and kurtosis is proposed, the extracted feature parameters are used as the input of support vector machine to classify and recognize the working conditions of oil pipelines. The collected data verify that the improved overall average empirical mode decomposition, Kalman filter, spread entropy and kurtosis combined recognition method can more accurately classify and recognize pipeline signals, and the results show that the total average recognition accuracy is 98.89. %, it provides a new way for the research of pipeline working condition identification.

Abstract:Accuracy and real-time measurement of slurry density in recycling box in wet desulphurization pulping system are important for the economic and stable operation of desulphurization process, a prediction model of slurry density in recycling box based on improved sparrow search algorithm optimization (ISSA) least squares support vector machine (LSSVM) is presented. Secondary variables that are highly correlated with the slurry density are selected and preprocessed through mechanism analysis, and use PCA algorithm to reduce dimension. Chaotic mapping and adaptive weights are added to the standard sparrow algorithm (SSA), which improves the uniformity of population distribution and searching ability of the algorithm. It is used to optimize the key parameters of LSSVM and to achieve accurate prediction of serum density. The simulation results of actual data have shown that the average absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of ISSA-LSSVM measurement model are reduced by 44.5%, 43.8%, 43.9% compared with SSA-LSSVM, and the prediction accuracy is significantly better than that of the pre-improvement prediction model, which has some engineering application value.