Journal of Engineering and Thermal Sciences
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Research on performance of piezoelectric superimposed beam energy harvester under gyromagnetic excitation
Get PDFIn this paper, In order to make use of the rotating motion characteristics of the roller cage shoe structure in vertical shaft lifting guide device, a nonlinear gyromagnetic excitation piezoelectric superposition beam energy harvester with compact structure, non-contact and high energy harvester efficiency is designed. The nonlinear force between moving magnet and stationary magnet is used to transform the rotating motion of the roller cage shoe into the end vibration of the piezoelectric vibrator. In order to improve the utilization rate of the piezoelectric plate and the low-amplitude response sensitivity of the energy harvester, acrylic material is introduced to re-place the traditional metal substrate, the bottom piezoelectric vibrator is hinged with the end of the top piezoelectric vibrator, and the middle part of the piezoelectric vibrator ends are longitudinal-superimposed beam structure with chute connection. The structural parameters, gyromagnetic excitation properties and strength of the piezoelectric vibrator are optimized and analyzed through finite element simulation and experiments, and the longitudinal and transverse arrays of piezoelectric vibrators are designed. The effects of terminal connection mode, array spacing, array number and load resistance on the power generation performance of the energy harvester are investigated. The results show that when the number of vertical arrays is 8 layers and the array spacing is 1.5 mm, higher voltage output and vibration consistency are achieved. When the horizontal array is 7 groups and the load is 10 kΩ, the maximum output power of the energy harvester is 11.26 mW, which can realize the self-power supply to the wireless transmitting node
Research on fault diagnosis method of electromechanical transmission system based on one-dimensional convolutional neural network with variable learning rate
Get PDFAs an important part of many mechanical equipment, the mechanical transmission system is very important to carry out efficient and accurate fault monitoring and diagnosis. Compared with traditional fault diagnosis techniques, such as spectrum analysis, deep learning has been widely used in the field of mechanical system fault diagnosis due to its powerful data expression ability, and has achieved certain research results. One-dimensional convolutional neural network is a widely used model for deep learning, so in this paper, the one-dimensional convolutional neural network (1D-CNN) in the deep learning theory and the vibration signal analysis method are integrated and applied to the fault identification of mechanical transmission system to achieve accurate diagnosis and classification of faults. The experiment is mainly to collect the vibration signal data of different fault states such as broken teeth, cracking, shaft unbalance, bearing wear, and excessive friction of the driven wheel of the mechanical transmission system, it was divided into training set and testing set according to an appropriate proportion, and 1D-CNN was built using Python. The deep learning model deeply analyzed the influence of different data sample sizes and different model parameters on the recognition accuracy, and obtained an ideal diagnostic model based on variable learning rate through parameter adjustment and comparative analysis. This experimental results show that the recognition method based on one-dimensional convolutional neural network can be effectively applied to the fault diagnosis of related mechanical transmission, and has a high diagnosis accuracy
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
The analysis of the destabilizing motion of a hyperbolic cooling tower during demolition blasting
Get PDFThe destabilizing motion characteristics of the hyperbolic cooling tower in demolition blasting are thoroughly investigated through the establishment of a numerical simulation calculation model, leading to the following conclusions regarding its destabilizing motion. The tensile-compression elastic-plastic model, which possesses the characteristics of parameter modification function and independence from unit size, can more effectively capture the mechanical properties of concrete materials and find better application in the simulation and calculation research of reinforced concrete structures. The self-oscillation frequency check and collapse morphological analysis are employed to validate the accuracy of the simulation calculation model for hyperbolic cooling towers, as well as to assess the rationality of parameters in the tensile-compression elastic-plastic model. The collapse of a cooling tower induces flexural deformation in the lateral wall, tensile disturbance in the upper and middle sections of the cylinder, and compressive disturbance in the vertical cross-section. The cylinder body has incurred damage as a result of the tower wall’s front end striking the ground at the directional window position on the front side of the throat, leading to a significant extrusion deformation issue. The buckling deformation in the central and lower sections of the rear wall propagated towards the back side of the tower wall upon reaching the ground, ultimately resulting in an “inverted V-shaped” damage along the buckling deformation line. The research findings hold significant relevance for future endeavors
Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform
Get PDFAs the continuous growth of the machinery industry, the importance of rolling bearings as key connecting parts in machinery movement is also increasing. However, the extraction and diagnosis of rolling bearing fault signals are difficult, and how to use modern transform analysis methods to raise the extraction efficiency and diagnostic accuracy becomes the focus. For this, a rolling bearing fault signal extraction and diagnosis model is designed based on empirical wavelet transform. The diagnostic model is optimized by using support vector machine and quantum genetic algorithm to design a rolling bearing fault signal extraction and diagnosis model based on improved empirical wavelet transform-support vector machine. The test results show that the research method can obtain four component signals showing different anomalies when generating time domain diagrams. Only five component peaks are generated and one group is extracted as output when generating component peaks. The abnormal amplitude of envelope spectrum basically reaches 0.40×10-6 or above. The judgment accuracy of component diagnosis reaches 98.12%. The above results show that the research method has better fault signal extraction ability and better diagnostic accuracy when performing fault signal diagnosis, which can provide new technical support for rolling bearing fault signal extraction and diagnosis
Prediction and evaluation of polymer turbulent drag reduction efficiency based on BP neural network
Get PDFIn the process of oil exploitation and transportation, in order to effectively predict and control energy consumption for drag reduction of oil flow, in this paper a BP neural network was proposed based method for predicting and evaluating the turbulent drag reduction efficiency of polymers, which can greatly improve the current situation of relying on empirical formulas and low generality in polymer turbulent drag reduction efficiency prediction. Based on the experimental data sets of four commercial polymer drag-reducing agents FLOXL, М-Flowtreat, Necadd-447, and FLO MXA, obtained at different polymer concentrations, viscosity, density, and Reynolds number, a BP neural network has been established and the optimal number of neurons in the hidden layer was selected using the root mean square error (RMSE) value to obtain the optimal BP neural network prediction model. The BP neural network prediction models for the four polymer drag-reducing agents all have a good fit of 0.98 or above, and the R2 of the trained BP neural network for the Necadd-447 drag-reducing agents is 0.9949, which is the best among the four polymer drag-reducing agents. The BP neural network established in this paper can be applied to the turbulent drag reduction transport of long-distance pipelines for oil products to achieve the prediction of the drag reduction efficiency of polymer additives
Stakeholder dynamics and their impact on value creation for industrial maintenance projects-a literature review
Get PDFThis paper analyses research developments in the dynamics of stakeholders and their impact mechanisms on the creation of value through a literature review. Three databases, Scopus, Science Direct and Google Scholar are selected to search articles. This study employs a quantitative descriptive analysis and a qualitative thematic analysis to provide a perspective of the data. The findings of the review reveal that stakeholder dynamics management is embedded in project environments and that the dynamic nature of the stakeholder salience attributes can be classified under stakeholder influence and engagement, project lifecycle and dynamics, value creation and framing, and project and stakeholder-associated risk. However, from the characterisation and the drivers of stakeholder dynamics discussed in the literature, the perspective of project risk dynamics has been understudied, with a focus mainly on stakeholder-associated risk to the project, and less on project risk and the stakeholder interactions related to potential losses or gains by stakeholders from such project decisions and activities. Although there is a recognition of the importance of managing stakeholder dynamics within project environments, the factors that affect stakeholder dynamics and their impact on the creation of value for industrial maintenance projects are still unclear. The outcome of the literature review can assist in providing the foundation for the authors’ empirical work of developing a novel conceptual framework for analysing stakeholder dynamics and their impact on maximising value creation in the context of industrial maintenance projects
Mechanism of time-delay feedback control of suspension damping with an annular vibration-absorbing structure
Get PDFWith the aim of enhancing both the ride comfort and the safety of the vehicle, we propose a new type of suspension with an annular vibration-absorbing structure, and establish a 3-DOF 1/4 vehicle model. The structure parameters and time-delay feedback control parameters are determined by particle swarm optimization algorithms, which take the root mean values of body acceleration, suspension dynamic deflection, and tire dynamic displacement as their optimization objectives. We analyze the stability of the suspension control system to ensure the stability of the time-delay control system through the Routh-Hurwitz stability criterion, characteristic root method, and stability switching method. Then, we compare and analyze the response characteristics of conventional suspension, new suspension without time-delay feedback control, and new suspension with time-delay feedback control under simple harmonic excitation and random excitation. The results show that the new suspension with time-delay feedback control has a significant damping effect on the body under the premise of ensuring the stability of the system
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
Vehicle state and parameter estimation based on improved extend Kalman filter
Get PDFIn order to reduce the influence of historical measurement data errors in the process of vehicle state estimation and improve the accuracy of the vehicle state estimation, a limited memory random weighted extended Kalman filter (LMRWEKF) algorithm is proposed. Firstly, a 3-DOF nonlinear vehicle dynamics model is established. Secondly, the limited memory extended Kalman filter is formed by fusing the limited memory filter and the extended Kalman filter. Then, according to the random weighting theory, the weighting coefficients that obey Dirichlet distribution are introduced to further improve the filtering estimation accuracy. Finally, a virtual test based on the ADAMS/CAR is used for the experimental verification. The results show that the error in the longitudinal velocity and the yaw rate is small, especially in the mean value of estimation error of side slip angle which is different in just 0.015 degrees between the virtual test and the simulation result. And also, the results compared with traditional methods indicate that the proposed LMRWEKF algorithm can solve the problem of vehicle state estimation with the performance of noise fluctuation suppression and higher estimation accuracy. The mean absolute error (MAE) and root mean square error (RMSE) are considered to verify the estimation accuracy of the proposed algorithm. And the comparison results indicate that the estimation accuracy of the LMRWEKF algorithm is significantly higher than those of the EKF and DEKF methods