JVE International
Not a member yet
1200 research outputs found
Sort by
Fault diagnosis algorithm based on GADF-DFT and multi-kernel domain coordinated adaptive network
Get PDFTo address the problems of low detection accuracy of rolling bearings under different loads and the difficulty of effectively identifying the lack of labelled data, a rolling bearing fault diagnosis method combining GADF-DFT image coding and Multi-kernel domain coordinated adaptation network is proposed. Firstly, the vibration signal is converted into a two-dimensional image using GADF coding technology, and then the GADF image is converted into the frequency domain using discrete Fourier transform to extract deeper feature information. Combined with the multi-source domain adaptive method, the public feature extraction module is used to initially achieve feature mining of the image; the MK-MMD algorithm of the domain-specific adaptive module reduces the difference in feature distribution between the source and target domains; and the final classification difference minimization module reduces the problems caused by the classification errors that may be generated by the different domain classifiers due to the fact that the data samples are located near the category boundaries. The test uses the Case Western Reserve University dataset and divides the dataset with different operating conditions as the source and target domains, and the test results show that the proposed model demonstrates its effectiveness in responding to the complex operating condition changes in rolling bearing fault detection in multiple operating condition migration tasks, good adaptability and robustness, and is able to achieve accurate fault diagnosis under different operating conditions
Research on high-speed railway vehicle emergency braking control based on rail surface foreign matter identification
Get PDFIn order to improve the operation stability of high-speed railway vehicles, an emergency braking control method for high-speed railway vehicles based on the identification of foreign matters on the track surface is proposed. The dynamic frame scanning method is used to build the image acquisition of abnormal objects on the high-speed rail track surface, and the visual sensor and vibration sensor are used to identify and process the features of foreign objects on the high-speed rail track surface. According to the abnormal feature extraction results, the command signal in case of foreign objects is transmitted to the terminal command library of the braking control system. According to the transfer switch of the emergency braking system to display the main emergency disposal output states such as the train emergency disposal information reception, emergency disposal level, emergency disposal type and other information, the joint control method of contact pressure distribution and non-uniform heat flux is adopted, and the joint control method of control system, drive system, disc friction system and signal acquisition system is adopted. The emergency braking control module is established to realize the emergency braking control of high-speed railway vehicles according to the feature recognition results of foreign matters on the rail surface. The test results show that the control module is less affected by the vehicle wheelbase characteristics, track structure characteristics and other factors when using this method for high-speed railway vehicle emergency braking control, and has a strong ability to identify the characteristics of foreign matters on the track surface, which improves the stability and real-time performance of emergency braking
Automatic vibration control method for grasping end of flexible joint robot
Get PDFBecause flexible robots have flexible components such as reducers, there are problems of accuracy deviation and end vibration in the process of external interference and trajectory tracking. This leads to the proposal of a Sliding Mode Control Approach Based on RBF Neural Network (SMC-RBF) parameter optimization. This method is mainly applied to reduce the end vibration and running position error of flexible robot. Firstly, the Newton-Euler method is used to establish the dynamic model of robot considering joint flexibility. At the same time, the experiment optimizes the Sliding Mode Control (SMC) method through RBF neural network. The experiments verify the control methods of the two-joint flexible robot and the six-joint flexible robot respectively. In the control of two-joint robot, the maximum tracking curve error of SMC is only about 0.25 rad under the interference of pulse signal; And the recovery time is only about 1 s. In the control of 6-joint robot, the maximum error of RBF-sliding mode control method on XYZ axis is 0.7 mm, 0.25 mm and 1.25 mm respectively; The error on three axes is smaller than that of traditional PD control method. The results demonstrate that the tracking error of the improved mode control is small, the chattering phenomenon of the robot system is weakened as well
Application of optimized CNN algorithm in landslide boundary detection
Get PDFLandslide, as a natural geological phenomenon with great harm, seriously threatens human social activities and life safety. It has a variety of latent and immeasurable destructiveness, which has a significant impact on the economic losses in rural areas. Therefore, it is urgent to take measures to accurately identify landslides to reduce their negative impacts. However, traditional manual visual interpretation has been unable to meet the current needs for emergency rescue of landslides, so computer intelligent methods have been paid attention to. This study proposes a new recognition network to address the problem of low accuracy of intelligent landslide boundary recognition methods. Firstly, the experiment incorporated boundary structure information into the Full Convolutional Network (FCN) for optimization, and constructed an Improved Full Convolutional Network (IFCN) model to better achieve image reconstruction. After that, Attention Mechanism (AM) is further introduced to achieve accurate detection of landslide boundaries in images, namely the IFCN-AM model. The attention mechanism introduced include spatial attention mechanism and multi-channel attention mechanism. Both are responsible for enhancing the language representation ability of the model and aggregating the interrelated features between different channels. The experimental results show that IFCN-AM has a 3 % to 7 % improvement in accuracy, recall, F1 value, and MIoU value
Electro-erosion fault identification of motor bearing based on ASMOTE-CFR
Get PDFIn view of the problem that the electro-erosion fault signal is rare and weak during motor operation, and the database is seriously imbalanced, this paper proposes an ASMOTE-CFR training model based on adaptive minority oversampling technology. Four bearing vibration acceleration signals in different states were collected through experiments, and each signal obtained 32 sets of energy features using wavelet packet decomposition. Then ASMOTE technology is used to balance the energy features of electro-erosion fault signal. And construct a vector matrix combined by energy features and bearing fault state features. Finally, the collaborative filter model of matrix decomposition is used to train and identify. The results show that the recognition rate of the ASMOTE-CFR model proposed in this paper is 98.46 %, which improves by 7 % compared with the traditional CFR, which verifies the effectiveness of this method
Influence of oral microbiome, gut microbiome and pulmonaire microbiome with COVID-19: review
Get PDFThe following document consists of a literature review that tries to relate oral, intestinal and pulmonary microbiome with complications of COVID-19, showing a significant part as a cause of death. The study tried to correlate viral respiratory infections with a second bacterial superinfection, which could be related to periodontitis
Concordance among three diagnostic methods for determining the position of the mandibular condyle
Get PDFThe position of the condyle can be determined using different diagnostic tools, among which are transcranial X-rays (RT), Cone Beam Computed Tomography (CBCT), and the Condylar Position Indicator (CPI). This study aimed to determine the concordance among the CPI, the CBCT, and the RT as diagnostic methods for determining the condyle position. Materials and method: 32 valid cases, each of which had an RT, a CBCT, and a CPI, were analyzed by three observers. SPSS V. 26.0 program was used to calculate the Kappa trust interval, using the jackknife technique which allows to estimate of the standard error between observers. To evaluate the condylar position using CPI, an AD2 articulator was employed. A Pullinger and Hollender layout was used in the tomographic and transcranial images. Results: The concordance among the three observers for each method resulted in a value of Kappa higher than 0.879. The concordance between CBCT and RT was 0.0627, between CPI and RT it was 0.247, and between CBCT and CPI it was .188. Conclusion: The level of concordance obtained between the three observers with any given diagnostic method was very good, which indicates that the concordance that each observer obtained regarding each diagnostic method is trustworthy. In this study, it is concluded that there is a concordance in the diagnosis regarding the position of the condyle of the tomography with the transcranial radiography. There is no concordance between tomography and CPI. There was also no concordance between the CPI and the transcranial radiography
Diagnosis of localized defects in floating bush bearings through time-frequency domain analysis
Get PDFBearings play a crucial role in the functionality of rotating machinery, and any defects in these components can result in machine failure. Detecting, diagnosing, and prognosing bearing faults are crucial steps in machine failure diagnostics to prevent malfunctions and breakdowns. While various methods exist for fault detection, including acoustic emission analysis, visual inspection, thermography, ultrasonic, motor current analysis, wear-debris analysis, oil analysis, and vibration analysis, the latter stands out as a popular non-destructive method. This paper focuses on time and frequency domain vibration analysis techniques for detecting faults in floating bush bearings. Particularly beneficial for online monitoring, remote and non-human intervention areas, and hazardous locations, the time-frequency domain approach enhances diagnostic capabilities. The vibration data collected during these experiments has been rigorously analyzed using data acquisition system and applied a comprehensive approach that includes evaluating the data in both the time and frequency domains, as well as utilizing advanced signal processing techniques, notably the high-frequency resonance technique. Test results underscore the effectiveness of specific parameters in identifying defects: waveform, form factor, parameter K, and cepstrum excel in pinpointing external defects, while kurtosis, crest factor, and skewness prove adept at identifying internal faults. In the frequency domain, the enveloped spectrum emerges as a robust method for comprehensive defect detection. The vibration data presented in this paper will prove to be an invaluable resource for professionals engaged in the field of vibration measurement and analysis
Exploring CO2 storage potential in Lithuanian deep saline aquifers using digital rock volumes: a machine learning guided approach
Get PDFThe increasing significance of carbon capture, utilization and storage (CCUS) as a climate mitigation strategy has underscored the importance of accurately evaluating subsurface reservoirs for CO2 sequestration. In this context, digital rock volumes, obtained through advanced imaging techniques such as micro-Xray computed tomography (MXCT), offer intricate insights into the porous and permeable structures of geological formations. This study presents a comprehensive methodology for assessing CO2 storage viability within Lithuanian deep saline aquifers, namely Syderiai and Vaskai, by utilizing petrophysical properties estimated from digital rock volumes of samples from analogous formations. It also demonstrates the potential of integrating advanced imaging techniques, machine learning, and numerical modeling for accurate assessment and effective management of subsurface CO2 storage
Full-scale shaking table test on seismic performance of a steel frame with translational-type precast-concrete exterior wall panel
Get PDFIn order to investigate the deformation behavior, mechanical characteristics of connections, and failure mode of translational precast concrete wall-panels under earthquake loading, a full-scale two-story steel frame model was designed. This test model consisted of 12 precast concrete wall-panels connected with fixed connections at the bottom end and translational-type connections at the top end. This model passed the shaking table testing, and the results demonstrate that the translational-type precast concrete exterior wall panel can ensure the sufficient relative displacement between the wall panel and the main structure under large earthquake loads. It has sufficient capacity to adapt the deformation between the main structure and the wall panels. During the test, localized cracking was observed on the wall panel, while all connections remained in elastic state. The connections between precast concrete exterior wall panels exhibited good performance, that indicates it is an effective tool to protect frame building hangings with exterior wall panels under strong earthquake events