Corn Harvester Bearing Fault Diagnosis Based on ABC-VMD and Optimized EfficientNet.

The extraction of the optimal mode of the bearing signal in the drive system of a corn harvester is a challenging task. In addition, the accuracy and robustness of the fault diagnosis model are low. Therefore, this paper proposes a fault diagnosis method that uses the optimal mode component as the input feature. The vibration signal is first decomposed by variational mode decomposition (VMD) based on the optimal parameters searched by the artificial bee colony (ABC). Moreover, the key components are screened using an evaluation function that is a fusion of the arrangement entropy, the signal-to-noise ratio, and the power spectral density weighting. The Stockwell transform is then used to convert the filtered modal components into time-frequency images. Finally, the MBConv quantity and activation function of the EfficientNet network are optimized, and the time-frequency pictures are imported into the optimized network model for fault diagnosis. The comparative experiments show that the proposed method accurately extracts the optimal modal component and has a fault classification accuracy greater than 98%.

Effects of Nano-CeO2 on Microstructure and Properties of WC/FeCoNiCrMo0.2 Composite High Entropy Alloy Coatings by Laser Cladding.

FeCoNiCrMo0.2 high entropy alloy has many excellent properties, such as high strength, high wear resistance, high corrosion resistance, and high ductility. To further improve the properties of this coating, FeCoNiCrMo high entropy alloy (HEA) coatings, and two composite coatings, FeCoNiCrMo0.2 + WC and FeCoNiCrMo0.2 + WC + CeO2, were prepared on the surface of 316L stainless steel by laser cladding technology. After adding WC ceramic powder and CeO2 rare earth control, the microstructure, hardness, wear resistance, and corrosion resistance of the three coatings were carefully studied. The results show that WC powder significantly improved the hardness of the HEA coating and reduced the friction factor. The FeCoNiCrMo0.2 + 32%WC coating showed excellent mechanical properties, but the distribution of hard phase particles in the coating microstructure was uneven, resulting in unstable distribution of hardness and wear resistance in each region of the coating. After adding 2% nano-CeO2 rare earth oxide, although the hardness and friction factor decreased slightly compared with the FeCoNiCrMo0.2 + 32%WC coating, the coating grain structure was finer, which reduced the porosity and crack sensitivity of the coating, and the phase composition of the coating did not change; there was a uniform hardness distribution, a more stable friction coefficient, and the flattest wear morphology. In addition, under the same corrosive environment, the value of polarization impedance of the FeCoNiCrMo0.2 + 32%WC + 2%CeO2 coating was greater, the corrosion rate was relatively low, and the corrosion resistance was better. Therefore, based on various indexes, the FeCoNiCrMo0.2 + 32%WC + 2%CeO2 coating has the best comprehensive performance and can extend the service life of 316L workpieces.

Rolling Bearing Fault Diagnosis Using Multi-Sensor Data Fusion Based on 1D-CNN Model.

To satisfy the requirements of the end-to-end fault diagnosis of rolling bearings, a hybrid model, based on optimal SWD and 1D-CNN, with the layer of multi-sensor data fusion, is proposed in this paper. Firstly, the BAS optimal algorithm is adopted to obtain the optimal parameters of SWD. After that, the raw signals from different channels of sensors are segmented and preprocessed by the optimal SWD, whose name is BAS-SWD. By which, the sensitive OCs with higher values of spectrum kurtosis are extracted from the raw signals. Subsequently, the improved 1D-CNN model based on VGG-16 is constructed, and the decomposed signals from different channels are fed into the independent convolutional blocks in the model; then, the features extracted from the input signals are fused in the fusion layer. Finally, the fused features are processed by the fully connected layers, and the probability of classification is calculated by the cross-entropy loss function. The result of comparative experiments, based on different datasets, indicates that the proposed model is accurate, effective, and has a good generalization ability.

Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings.

We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD-RCMDE-SSA-SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.

Fault Diagnosis of a Wind Turbine Gearbox Based on Improved Variational Mode Algorithm and Information Entropy.

The working environment of wind turbine gearboxes is complex, complicating the effective monitoring of their running state. In this paper, a new gearbox fault diagnosis method based on improved variational mode decomposition (IVMD), combined with time-shift multi-scale sample entropy (TSMSE) and a sparrow search algorithm-based support vector machine (SSA-SVM), is proposed. Firstly, a novel algorithm, IVMD, is presented for solving the problem where VMD parameters (K and α) need to be selected in advance, which mainly contains two steps: the maximum kurtosis index is employed to preliminarily determine a series of local optimal decomposition parameters (K and α), then from the local parameters, the global optimum parameters are selected based on the minimum energy loss coefficient (ELC). After decomposition by IVMD, the raw signal is divided into K intrinsic mode functions (IMFs), the optimal IMF(s) with abundant fault information is (are) chosen based on the minimum envelopment entropy criterion. Secondly, the time-shift technique is introduced to information entropy, the time-shift multi-scale sample entropy algorithm is applied for the analysis of the complexity of the chosen optimal IMF and extract fault feature vectors. Finally, the sparrow search algorithm, which takes the classification error rate of SVM as the fitness function, is used to adaptively optimize the SVM parameters. Next, the extracted TSMSEs are input into the SSA-SVM model as the feature vector to identify the gear signal types under different conditions. The simulation and experimental results confirm that the proposed method is feasible and superior in gearbox fault diagnosis when compared with other methods.

Location Determination of Impact on the Wind Turbine Blade Surface Based on the FBG and the Time Difference.

This paper proposes an approach to the determination of the precise location of an impact on the surface of a wind turbine blade (WTB) based on a fiber Bragg grating (FBG) and the time difference, and its effectiveness is verified by experiments. First, the strain on the WTB surface is detected with an FBG. Then, the signal is decomposed into a series of components via variational mode decomposition (VMD), and some signals with impact characteristics are chosen for reconstruction. The instant energy of the reconstructed signal is then amplified through the Teager energy operator (TEO) to identify the time difference between FBGs. Finally, the coordinate of the impact point is obtained by solving the hyperbolic mode with the time difference. The results of experiments demonstrate that the proposed approach exhibits good performance with high accuracy (97%) and low error (12.3 mm).

Microrna-145 accelerates the inflammatory reaction through activation of NF-κB signaling in atherosclerosis cells and mice.

Atherosclerosis (AS) is a chronic inflammation, which is a major cause of morbidity and mortality in the world. Accumulative evidences have demonstrated that miRNAs exert crucial roles in the development of AS. However, the effects of miR-145 and its underlying molecular mechanism remain incompletely clear. The aim of the present study is to explore the function of miR-145 in the occurrence and development of AS through investigating its role in inflammatory reactions. High-fat diet (HFD)-treated ApoE-/- mice were used as an in vivo model of atherosclerosis (AS). OxLDL-induced macrophages was employed as cell models of atherosclerosis. RT-PCR was used to evaluate the transfected efficiency of miR-145 mimic and inhibitor. RT-PCR and ELISA were performed to detect the expression of miR-145, and inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), C-C motif chemokine ligand 2 (CCL-2), CCL-4 and CCL-7. Western blotting was used to evaluate the protein expression of nuclear factor κB (NF-κB) and its related proteins such as phosphorylated-signal transducer and activator of transcription 3 (p-STAT3), p-IκBα and acetylated p65 (ac-p65). Hematoxylin and eosin (H&E) staining were conducted to examine atherosclerotic lesion. Immunohistochemistry was carried out to detect the expression of α-smooth muscle Actin (α-SMA) and CD68. Luciferase reporter assay were carried out to examine the effect of miR-145 on the transcriptional activity of NF-κB. Our results showed that over-expression of miR-145 promoted the expression of IL-1ß, TNF-α, CCL-2, CCL-4 and CCL-7 through promotion of NF-κB, p-IκBα, p-STAT3 and ac-p65 expression in vivo and in vitro. Besides, down-regulation of miR-145 expression relieved the aortic sinus lesion, increased the number of VSMCs and decreased the number of macrophages. In conclusion, our study demonstrated that miR-145 accelerated the inflammatory reaction through activation of NF-κB signaling in AS.