A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy.

The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings.

Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf Algorithm.

This study targets the low accuracy and efficiency of the support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of SVM and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor α. Thus, the search ability was balanced at different early and late stages by controlling the dynamic changes of the variable coefficient. In the early stages of optimization, its speed is low to avoid falling into local optimization. In the later stages of optimization, the speed is higher, and finding the optimal solution is easier, balancing the two different global and local optimization capabilities to complete efficient convergence. The dynamic weight update strategy was adopted to perform position updates based on adaptive dynamic weights. First, the dataset of Case Western Reserve University was used for simulation, and the results showed that the diagnosis accuracy of IGWO-SVM was 98.75%. Then, the IGWO-SVM model was trained and tested using data obtained from the full-life-cycle test platform of mechanical transmission bearings independently researched and developed by Nanjing Agricultural University. The fault diagnosis accuracy and convergence value of the adaptation curve were compared with those of PSO-SVM (particle swarm optimization) and GWO-SVM diagnosis models. Results showed that the IGWO-SVM model had the highest rolling bearing fault diagnosis accuracy and the best diagnosis convergence.

Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis.

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis. To optimize the SVM, we construct a hybrid kernel SVM using a polynomial kernel function and radial basis kernel function. BO is used to optimize the extreme values of the objective function and determine their weight coefficients. We create an objective function for the Gaussian regression process of BO using training and test data as inputs, respectively. The optimized parameters are used to rebuild the SVM, which is then trained for network classification prediction. We tested the proposed diagnostic model using the bearing dataset of the Case Western Reserve University. The verification results show that the fault diagnosis accuracy is improved from 85% to 100% compared with the direct input of vibration signal into the SVM, and the effect is significant. Compared with other diagnostic models, our Bayesian-optimized hybrid kernel SVM model has the highest accuracy. In laboratory verification, we took sixty sets of sample values for each of the four failure forms measured in the experiment, and the verification process was repeated. The experimental results showed that the accuracy of the Bayesian-optimized hybrid kernel SVM reached 100%, and the accuracy of five replicates reached 96.7%. These results demonstrate the feasibility and superiority of our proposed method for fault diagnosis in rolling bearings.

Quality Grading of River Crabs Based on Machine Vision and GA-BPNN.

The prices of different quality river crabs on the market can vary several times. Therefore, the internal quality identification and accurate sorting of crabs are particularly important for improving the economic benefits of the industry. Using existing sorting methods by labor and weight to meet the urgent needs of mechanization and intelligence in the crab breeding industry is difficult. Therefore, this paper proposes an improved BP neural network model based on a genetic algorithm, which can grade the crab quality. We comprehensively considered the four characteristics of crabs as the input variables of the model, namely gender, fatness, weight, and shell color of crabs, among which gender, fatness, and shell color were obtained by image processing technology, whereas weight is obtained using a load cell. First, mature machine vision technology is used to preprocess the images of the crab's abdomen and back, and then feature information is extracted from the images. Next, genetic and backpropagation algorithms are combined to establish a quality grading model for crab, and data training is conducted on the model to obtain the optimal threshold and weight values. Analysis of experimental results reveals that the average classification accuracy reaches 92.7%, which proves that this method can achieve efficient and accurate classification and sorting of crabs, successfully addressing market demand.

Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM.

A rolling bearing fault diagnosis method based on whale gray wolf optimization algorithm-variational mode decomposition-support vector machine (WGWOA-VMD-SVM) was proposed to solve the unclear fault characterization of rolling bearing vibration signal due to its nonlinear and nonstationary characteristics. A whale gray wolf optimization algorithm (WGWOA) was proposed by combining whale optimization algorithm (WOA) and gray wolf optimization (GWO), and the rolling bearing signal was decomposed by using variational mode decomposition (VMD). Each eigenvalue was extracted as eigenvector after VMD, and the training and test sets of the fault diagnosis model were divided accordingly. The support vector machine (SVM) was used as the fault diagnosis model and optimized by using WGWOA. The validity of this method was verified by two cases of Case Western Reserve University bearing data set and laboratory test. The test results show that in the bearing data set of Case Western Reserve University, compared with the existing VMD-SVM method, the fault diagnosis accuracy rate of the WGWOA-VMD-SVM method in five repeated tests reaches 100.00%, which preliminarily verifies the feasibility of this algorithm. In the laboratory test case, the diagnostic effect of the proposed fault diagnosis method is compared with backpropagation neural network, SVM, VMD-SVM, WOA-VMD-SVM, GWO-VMD-SVM, and WGWOA-VMD-SVM. Test results show that the accuracy rate of WGWOA-VMD-SVM fault diagnosis is the highest, the accuracy rate of a single test reaches 100.00%, and the accuracy rate of five repeated tests reaches 99.75%, which is the highest compared with the above six methods. WGWOA plays a good optimization role in optimizing VMD and SVM. The signal decomposed by VMD is optimized by using the WGWOA algorithm without mode overlap. WGWOA has the better convergence performance than WOA and GWO, which further verifies its superiority among the compared methods. The research results can provide an effective improvement method for the existing rolling bearing fault diagnosis technology.

Analysis of Factors Influencing Vibration Suppression of Spray Boom-Air Suspension for Medium and Small-Scale High-Clearance Sprayers.

Medium and small-scale high-clearance sprayers are widely applied in medium and small-scale farms. Owing to power and load limitations, it is difficult to manage the complex system for suppressing spray boom vibration. This study was conducted to design a spray boom-air suspension system suitable for medium and small-size high-clearance sprayers by combining spray boom vibration suppression and the characteristics of air spring charging/discharging. Thus, this study aims to address the non-homogeneous distribution of spray triggered by the spray boom vibrations in medium and small high-clearance sprayers. The effects of different elastic elements on the vibration suppression effect of the spray boom were compared. According to the bench test, the dynamic response results of the spray boom under transient and sinusoidal excitations indicate that air spring is more conducive to vibration suppression than coil spring. The results obtained from the field experiments indicate that under the low solid soil condition, the spray boom air suspension should match a small additional air chamber with a volume of approximately 0.6 L, and the damping coefficient of the damper should be approximately 1792 N·s/m. In the case of the high firm soil, the spray boom air suspension should match a large additional air chamber with a volume of approximately 3.6 L, while the damping coefficient of the damper should be set to approximately 1316 N·s/m. The soil with low moisture content and high firmness are unfavorable to the vibration suppression of the spray boom. This study provides a reference for enhancing the vibration suppression of the spray boom-air suspension and improving the spray uniformity of the sprayer.

Study on Properties of 45 Carbon Steel Ni-P Electroless Plating Reinforced by Si3N4-Al2O3 Particle Based on Response Surface Method.

The effects of the different content of Si³N4 particles and Al²O³ particles in the plating solution and the different ratios on the wear resistance, microhardness, corrosion resistance and other properties of the coating were analyzed by using the centre composite surface design of response surface method (RSM). Meanwhile the phase composition, appearance, microhardness, friction coefficient and corrosion resistance of the electroless coating were tested. The results show that the addition of Al²O³ and Si³N4 particles in the bath can increase the microhardness of the electroless composite coating. In a certain range, the increase of Al²O³ or Si³N4 particles in the bath causes the microhardness of the coating to increase, but the excessive addition of particles makes microhardness decrease; the electroless coating with two particles added will have a low coefficient of friction; and with respect to corrosion resistance, the addition of Al²O³ or Si³N4 particles will increase the corrosion resistance of the coating. Overall, the electroless coating with the Al²O³ content of 16 g/L and the Si³N4 content of 12.63 g/L has the better comprehensive performance.