Fretting wear analysis of spline couplings in agricultural tractor with axis deviation

The spline pair needs to transmit large alternating torque and other directional loads, which causes the fretting wear of the spline pair to be serious, which leads to the failure of the spline pair connection and reduces the reliability of the entire transmission system. Therefore, it is of great significance to carry out research on fretting wear of spline pairs and improve the ability of splines to resist fretting wear. In this paper, based on the finite element method, a model considering the tooth fretting wear property of the agricultural tractor spline couplings model was developed to analyze changes of contact stress and relative slip distributions, in which the axis deviation was considered. The results show that axis deviation significantly increases the value of contact stress and relative slip in the spline couplings. With the increasing deviation, the value of contact stress and relative slip slightly raise accordingly. The friction coefficient shall not be too small when the system is lubricated. As a result, maintenance of the agricultural tractor transmission system can be required

Bearing fault feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise

As an important part of rotating machinery, bearings play an important role in large-scale mechanical equipment. Abnormal bearing conditions may cause the machine to malfunction, or even evolve into a serious accident. Therefore, the accurate and timely fault diagnosis of the bearing is of great significance. Based on EMD, this paper introduces the working principles and characteristics of EEMD and CEEMDAN, respectively. Then the signal was decomposed by EEMD and CEEMDAN respectively. The simulation results show that CEEMDAN has better effect on signal decomposition. Then, comparing the effect of CEEMDAN and EEMD on bearing fault feature frequency extraction, the experiment proves that CEEMDAN has a better ability to preserve original signal and eliminate noise than EEMD method, and can extract bearing fault feature more accurately and timely

Technology of Microclimate Regulation in Organic and Energy-Sustainable Livestock Production

The control of climatic conditions where cattle are kept is one of the challenges in the livestock sector regarding the digital automation of the process. (1) Background: The main purpose of this study is to define the optimal foundations for automatic climatic systems in organic and energy-sustainable livestock production. In particular, the following components are suggested: (a) the determination of current deviations and interdependency between factors; (b) an algorithm for defining the possible sources of regulation; (c) the ranking approach of the optimal sequence of possible sources; and (d) ensuring transparency and coordination of the model with organic and energy certificates. (2) Methods: This investigation accumulates information on the characteristics of the main microclimatic parameters and simulates their possible combinations in a livestock building in Poland within 24 h of a spring day. A few indices are considered that signal the impact on the thermal comfort of cattle based on the example of recommended measures for the Angus steer genotype. (3) Results: The proposed transparent algorithm is designed for selecting and ranking potential sources of microclimate control according to three criteria. (4) Conclusions: This paper potentially contributes to determining the most optimal digital algorithm for managing microclimate conditions to ensure acceptable comfort for animals, meeting the requirements of organic certification with minimum costs of production, and switching to sustainable types of energy with consideration of technologies’ efficiency. The algorithm is scalable and adjustable to the individual conditions of any livestock premise with a digitally controlled environment

Empirical models for estimating monthly global solar radiation: a most comprehensive review and comparative case study in China

Global solar radiation is a core component of scientific research and engineering application across a broad spectrum. However, its measurement is limited by a small number of stations due to the technical and financial restricts. Estimating solar radiation with the meteorological variables using empirical models is of benefit to obtain solar radiation data at global scale. Yet, there are various options of available empirical models to select the most suitable one. This study conducted a most comprehensive collection and review of empirical models employing the commonly measured meteorological variables and geographic factors. A total of 294 different types of empirical models were collected and classified into 37 groups according to input attributes. Such collection built an empirical model library providing an overall overview of the developed empirical models in literatures. Furthermore, the collected models were calibrated and evaluated at three meteorological stations in the Three Gorges Reservoir Area in China. This study suggests that these model-comparing processes can assist the governments, scientists and engineers in tailoring the most fitted model for specific applications and in particular areas

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

The Influence of Bit Edge Shape Parameters on Bone Drilling Force Based on Finite Element Analysis

Bone drilling is a common surgery procedure. The drill bit shape directly affects the drilling force. Excessive drilling force may cause bone damage. In this work, on the premise of analyzing and comparing several finite element method (FEM) simulation results for drill bit of 5 mm in diameter commonly used in medical practice, a combination of drilling speed and feed rates which can minimize the drilling force for drilling parameters is determined. Then, the effects of the drill bit shape parameters including helix angle, point angle and edge radius on the drilling force are simulated by using the obtained drilling parameters, and after validation the FEM analysis results show that their variation trend is the same as the experimental one. Then, the optimum bit structure parameters are obtained through the following research: (1) the prediction model of the relationship between drill edge parameters and drilling force is established based on the result of FEM of the drilling process; (2) A particle swarm optimization algorithm is used to obtain the optimal matching parameters of the bit structure; (3) The priority order of the influence of the parameters of the bit on the drilling force is analyzed. The results show that the order of the influence is: the edge radius is the largest, the point angle is the second, and the helix angle is the smallest. The optimum combination of bit structure is that point angle, helix angle and edge radius are 95°, 35°, and 0.02 mm, respectively

Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising

Rolling bearings are the core components of the machine. In order to save costs and prevent accidents caused by bearing failures, the rolling bearing fault diagnosis technology has been widely used in the industrial field. At present, the proposed methods include wavelet transform, morphological filtering, empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD), which have obvious shortcomings. As it is difficult to extract the fault characteristic frequency caused by nonlinear and nonstationary features of the rolling bearing fault signal, this paper presents a fault feature extraction method of rolling bearing based on nonlinear mode decomposition (NMD) and wavelet threshold denoised method. First of all, the fault signal was preprocessed via wavelet threshold denoising. Then, the denoised signal was decomposed by using NMD. Next, the mode component envelope spectrum was made. Finally, the fault characteristic frequency of rolling bearing was extracted. The method was compared with EMD through the simulation experiment and rolling bearing fault experiment. Meanwhile, two indicators including signal-noise ratio (SNR) and root-mean-square error (RMSE) were also established to evaluate the fault diagnosis ability of this method, and the results show that this method can extract the fault characteristic frequency accurately

Study on the reliability of plugging and detaching fisheye contact

Aiming at contact instability and the deformation problem of possible contact between the fisheye structure and the through hole in the fisheye shaped contact pairs, for pair alignment of fisheye shaped pairs, simulation analysis and experimental research of plug and play is conducted in this paper. Firstly, the important parameters of each structure of fisheye shaped contact pairs are analyzed, then, according to analyze the influence factors of fisheye shaped contact on deformation and insertion force, and based on ABAQUS software to study plug and simulate fisheye shaped contact under different parameters, it is concluded that the insertion force, structural deformation and signal transmission of fisheye shaped contact have great influence on the contact force of fisheye under different material parameters and structural parameters. Multiple plug experiment on fisheye shaped contact pairs was conducted, and the reliability of plug and socket contact insertion has significant influence on plug and play reliability was obtained, thus theoretical basis for the design of fisheye shaped structure, the setting of tolerance range and the control of frequency of use was provided.</p

Research on fault feature extraction method of rolling bearing based on NMD and wavelet threshold denoising

Rolling bearings are the core components of the machine. In order to save costs and prevent accidents caused by bearing failures, the rolling bearing fault diagnosis technology has been widely used in the industrial field. At present, the proposed methods include wavelet transform, morphological filtering, empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD), which have obvious shortcomings. As it is difficult to extract the fault characteristic frequency caused by nonlinear and nonstationary features of the rolling bearing fault signal, this paper presents a fault feature extraction method of rolling bearing based on nonlinear mode decomposition (NMD) and wavelet threshold denoised method. First of all, the fault signal was preprocessed via wavelet threshold denoising. Then, the denoised signal was decomposed by using NMD. Next, the mode component envelope spectrum was made. Finally, the fault characteristic frequency of rolling bearing was extracted. The method was compared with EMD through the simulation experiment and rolling bearing fault experiment. Meanwhile, two indicators including signal-noise ratio (SNR) and root-mean-square error (RMSE) were also established to evaluate the fault diagnosis ability of this method, and the results show that this method can extract the fault characteristic frequency accurately.</p

Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network

Rolling bearings play a pivotal role in rotating machinery. The remaining useful life prediction and fault diagnosis of bearings are crucial to condition-based maintenance. However, traditional data-driven methods usually require manual extraction of features, which needs rich signal processing theory as support and is difficult to control the efficiency. In this study, a bearing remaining life prediction and fault diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN) has been proposed. First, the STFT was adopted to construct time-frequency maps of the unprocessed original vibration signals that can ensure the true and effective recovery of the fault characteristics in vibration signals. Then, the training time-frequency maps were used as an input of the CNN to train the network model. Finally, the time-frequency maps of testing signals were inputted into the network model to complete the life prediction or fault identification of rolling bearings. The rolling bearing life-cycle datasets from the Intelligent Management System were applied to verify the proposed life prediction method, showing that its accuracy reaches 99.45%, and the prediction effect is good. Multiple sets of validation experiments were conducted to verify the proposed fault diagnosis method with the open datasets from Case Western Reserve University. Results show that the proposed method can effectively identify the fault classification and the accuracy can reach 95.83%. The comparison with the fault diagnosis classification effects of backpropagation (BP) neural network, particle swarm optimization-BP, and genetic algorithm-BP further proves its superiority. The proposed method in this paper is proved to have strong ability of adaptive feature extraction, life prediction, and fault identification