Survey and Recommendations for Land Use of Agricultural Machinery Service Organization Facilities in Guizhou Province

With the advancement of agricultural modernization, agricultural machinery service organizations, as an important part of the rural operating entities, show an increasing trend year by year, and need new facilities land in the process of development and growth. Based on the written survey of 597 agricultural machinery service organizations in Guizhou Province, the demand and demand gap of various facilities land use in agricultural machinery service organizations were analyzed and compared. In addition, based on the field survey and policy background, the main practices and experiences of agricultural machinery service organizations in Guizhou Province in the approval and use of facility land were sorted out, and the problems and causes of large gap in facility agricultural land, lack of standards and difficulty in policy implementation were analyzed. Finally, it recommended that the site selection of agricultural machinery service organization facilities needs to be standardized, the land demand needs to be coordinated, the land scale needs to be scientifically demonstrated, and financial support should be sought to promote Guizhou agricultural machinery service organization to become bigger, better and stronger.

Research on Rotating Machinery Fault Diagnosis Based on Improved Multi-target Domain Adversarial Network

Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.

A stepwise optimization method for topology structure of fluid machinery network

The circulating water system is widely used as the cooling system in the process industry,which has the characteristics of high water and power consumption,and its energy consumption level has an important impact on the economic performance of the whole system.Pump network and water turbine network constitute the work network of the circulating water system,that is,the fluid machinery network.Based on the previous studies,this paper proposes a stepwise method to optimize the fluid machinery network,that is,to optimize the network structure by using the recoverable pressure-head curve of the branch,and consider the recovery of adjustable resistance at the valve of each branch,so as to further reduce energy consumption and water consumption.The calculation result of the case shows that the topology structure optimization can further reduce the operation cost and the annual capital cost on the basis of the fixed structure optimization,and the total annualized cost can be reduced by 30.04%.The optimization result of different flow shows that both the pump network and the water turbine network tend to series structure at a low flow rate whereas to parallel structure at a high flow rate.

Weak Fault Feature Extraction of the Rotating Machinery Using Flexible Analytic Wavelet Transform and Nonlinear Quantum Permutation Entropy

Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform(FAWT)with Nonlinear Quantum Permutation Entropy.FAWT,leveraging fractional orders and arbitrary scaling and translation factors,exhibits superior translational invariance and adjustable fundamental oscillatory characteristics.This flexibility enables FAWT to provide well-suited wavelet shapes,effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults.In our approach,gearbox vibration signals undergo FAWT to obtain sub-bands.Quantum theory is then introduced into permutation entropy to propose Nonlinear Quantum Permutation Entropy,a feature that more accurately characterizes the operational state of vibration simulation signals.The nonlinear quantum permutation entropy extracted from sub-bands is utilized to characterize the operating state of rotating machinery.A comprehensive analysis of vibration signals from rolling bearings and gearboxes validates the feasibility of the proposed method.Comparative assessments with parameters derived from traditional permutation entropy,sample entropy,wavelet transform(WT),and empirical mode decomposition(EMD)underscore the superior effectiveness of this approach in fault detection and classification for rotating machinery.

Exploration of Teaching Reform of Food Machinery and Equipment Course Based on New Engineering Concept

The new engineering concept aims to train high-quality engineering talents to meet the needs of future science and technology and industrial development through the reform of education and teaching.Under the background of"new engineering",by introducing cutting-edge knowledge of the industry and interdisciplinary integration,adopting innovative teaching methods such as project-driven teaching and flipped classroom,strengthening experimental teaching and school-enterprise cooperation,and establishing comprehensive evaluation and feedback mechanism,Food Machinery and Equipment course is reformed to improve the teaching quality and train high-quality engineering talents to meet the needs of modern food processing industry.

Contribution of Agricultural Machinery Automation to Sustainable Agricultural Development

With the rapid growth of the global population and the increasing challenges posed by climate change and labor shortages,traditional agricultural methods are becoming increasingly unsustainable.Agricultural machinery automation presents a promising solution by leveraging cutting-edge technologies such as artificial intelligence(AI),the Internet of Things(IoT),and robotics to enhance agricultural productivity,optimize resource utilization,and reduce environmental impacts.This study explores the evolution of agricultural machinery automation,the core technologies involved,and its applications in sustainable agriculture.Key benefits of automation include improved precision in farming practices,better resource efficiency,reduced environmental degradation,and enhanced economic sustainability.Despite its potential,several barriers hinder the widespread adoption of agricultural automation,including technological limitations,high initial costs,and regulatory challenges.This research also offers recommendations to accelerate the adoption of agricultural automation,including financial support,infrastructure development,and capacity building for farmers.As technological advancements continue,agricultural machinery automation is poised to play a critical role in shaping the future of sustainable agriculture.

Technological Progress and Application Prospects of Unmanned Agricultural Machinery

With the continuous growth of the global population and the acceleration of urbanization,traditional agriculture faces severe challenges,such as labor shortages,low efficiency in agricultural resource utilization,and uncertainties caused by climate change.Unmanned agricultural machinery,as an integral part of smart agriculture,utilizes automation and intelligent technologies to achieve precise control over the entire agricultural production process,improving production efficiency,reducing labor costs,minimizing environmental impacts,and enhancing sustainability.In recent years,with advancements in information technology,artificial intelligence,and the Internet of Things,unmanned agricultural machinery has made significant technological progress and has gradually entered the commercialization stage.This study aims to analyze the technological advancements and development trends of unmanned agricultural machinery and explore its application prospects in practical production.By investigating this emerging technology,the research not only provides a theoretical foundation for agricultural technological innovation but also offers practical references for policymakers,agricultural enterprises,and practitioners.Particularly in the critical period of global agricultural transformation,research on unmanned agricultural machinery holds significant economic,environmental,and social value.

Interview with Alex Zucchi, President ofthe European Committee of Textile Machinery Manufacturers

CT:What do you think is the importance of the Chinese market for European manufacturers of textile machinery?Alex Zucchi:There is no doubt the Chinese market remains of primary importance to European textile machinery manufacturers:While it is acknowledged that the local producers have increased the quality of their machinery significantly in recent years,European textile technology manufacturers have been continuously investing in research to develop exciting innovations and to further improve existing technologies.Consequently,these new and more advanced technologies benefit not only Chinese manufacturers but the global textile industry.

Stäubli:Empowering textile machinery to promote sustainable development

Stäubli,with over a century of tech¬nology accumulation and process innovation,is at the forefront of rapid change in the global textile industry.With excellent textile machin-ery technology and expertise in mechatronics,Stäubli continues to bring efficient and reliable solutions to customers around the world。

Italian textile machinery orders remain stable in Q1 2024

For Italian textile machinery sector,2024 has begun without anything seemingly special.The first quarter has seen the orders index,as reported by the Economics Department of ACIMIT-the Association of Italian Textile Machinery Manufacturers-remain stationary compared to the same period the previous year.In absolute terms,the index came in at 61.2 points(basis:2021=100).

Summary and Improvement Measures for Soil Compaction Caused by Machinery

As agricultural mechanization is becoming more and more popular, soil compaction, on basis of agricultural machinery, has become a serious problem that can not be ignored. Soil compaction, which is caused by frequent til age and large load in the field, may have different effects on various properties of soil. Soil com-paction may result in different conditions, such as increased soil density and the mechanical resistance, and decreased soil ventilation and the capacity of water holding and storage, but uptaking capacity of chemical elements is restricted. There-fore, soil compaction has some negative impacts on soil properties, physical y, chemical y, or biological y, as wel as plant growth. This research analyzed the cause and the harm of soil compaction in recent years, and some effective mea-sures were proposed to improve soil compaction, in order to reduce the extent of soil compaction caused by agricultural machinery.

Technical Regulation of Foxtail Millet Production by Combining Machinery and Agronomy

In order to solve the problems of low production efficiency,great loss and low yield,Millet Research Institute of Hebei Academy of Agriculture and Forestry Sciences integrated the plastic film mulching technique and mechanized production technique,forming a foxtail millet production technique combining machinery and agronomy.The foxtail millet production technique combining machinery and agronomy regulates millet production from the links of soil preparation,fertilization,variety selection,seeding,intertillage and fertilization and harvest,so as to achieve the effects of promoting the matching between agro-machinery and agronomy,improving the level of millet production mechanization,realizing light simplified production and saving labor cost.This technical regulation has a broad application prospect.

HIGHER ORDER SPECTRA AND ITS APPLICATION IN MACHINERY FAULT DIAGNOSIS

The application of higher order spectra to machinery faults diagnosis is studied in this paper.A brief review of bispectra is presented,and more emphasis is placed on the ability of higher order spectra to extract diagnostic information from fault signals.Furthermore,by use of the algorithm of higher order spectra,two kinds of typical mechanical faults are analyzed.Results show that the high order spectra analysis is a more efficient method in machinery diagnosis compared with the FFT based spectral analysis.

Analysis on the Mode of Trans-Regional Allocation of Agricultural Machinery

With the continuous progress of agricultural technology, agricultural mechanization presents a good development trend after half a century development. But agricultural machinery equipment and the level of agricultural mechanization are imbalances in different regions; the structure of agricultural machinery equipment is unreasonable; there are great differences in agricultural mechanization level of different operations and different crops and the whole level of agricultural mechanization social service is not high. Through cross regional allocation of existing agricultural machinery to improve the working efficiency of agricultural machinery;we can extend the operation time of the agricultural machinery and improve the operation effect and income of agricultural machinery.

MACHINERY SUB-COUNCIL OF THE CCPIT——A WINDOW TO THE OUTSIDE WORLD FOR CHINA'S MACHINERY INDUSTRY

The Machinery Sub-council ofthe CCPIT (China Council forthe Promotion of InternationalTrade)was among the six firstestablished sub-councils.In lessthan eight years since itsestablishment,the Machinery Sub-council of the CCPIT has performedsuccessfully as a window to theoutside world for the machineryindustry,in coordination with thedevelopment strategy of foreigneconomic and trade cooperationfor the machinery industry sector,in promoting economic and tradeexchange between Chinese andoverseas enterprises,and makingChina’s machinery industryaccessible to the world market.

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

According to statistic data,machinery faults contribute to largest proportion of High-voltage circuit breaker failures,and traditional maintenance methods exist some disadvantages for that issue.Therefore,based on the wavelet packet decomposition approach and support vector machines,a new diagnosis model is proposed for such fault diagnoses in this study.The vibration eigenvalue extraction is analyzed through wavelet packet decomposition,and a four-layer support vector machine is constituted as a fault classifier.The Gaussian radial basis function is employed as the kernel function for the classifier.The penalty parameter c and kernel parameterδof the support vector machine are vital for the diagnostic accuracy,and these parameters must be carefully predetermined.Thus,a particle swarm optimizationsupport vector machine model is developed in which the optimal parameters c andδfor the support vector machine in each layer are determined by the particle swarm algorithm.The validity of this fault diagnosis model is determined with a real dataset from the operation experiment.Moreover,comparative investigations of fault diagnosis experiments with a normal support vector machine and a particle swarm optimization back-propagation neural network are also implemented.The results indicate that the proposed fault diagnosis model yields better accuracy and e-ciency than these other models.

Demodulation Based on Harmonic Wavelet and Its Application into Rotary Machinery Fault Diagnosis

The harmonic wavelet transform（HWT） and its fast realization based on fast Fourier transform（FFT） are introduced. Its ability to maintain the same amplitude-frequency feature is revealed. A new method to construct the time-frequency（TF） spectrum of HWT is proposed, which makes the HWT TF spectrum able to correctly reflect the time-frequency-amplitude distribution of the signal. A new way to calculate the HWT coefficients is proposed. By zero padding the data taken out, the non-decimated coefficients of HWT are obtained. Theoretical analysis shows that the modulus of the coefficients obtained by the new calculation way and living at a certain scale are the envelope of the component in the corresponding frequency band. By taking the cross section of the new TF spectrum, the demodulation for the component at a certain frequency band can be realized. A comparison with the Hilbert demodulation combined with band-pass filtering is done, which indicates for multi-components, the method proposed here is more suitable since it realizes ideal band-pass filtering and avoids pass band selecting. In the end, it is applied to bearing and gearbox fault diagnosis, and the results reflect that it can effectively extract the fault features in the signal.

Fault prediction method for nuclear power machinery based on Bayesian PPCA recurrent neural network model

Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated probabilistic principal component analysis(PPCA), multi-resolution wavelet analysis, Bayesian inference, and RNN model for nuclear power machinery that consider data uncertainty and chaotic time series. After denoising the source data, the Bayesian PPCA method is employed for dimensional reduction to obtain a refined data group. A recurrent neural network(RNN) prediction model is constructed, and a Bayesian statistical inference approach is developed to quantitatively assess the prediction reliability of the model. By modeling and analyzing the data collected on the steam turbine and components of a nuclear power plant, the results of the goodness of fit, mean square error distribution, and Bayesian confidence indicate that the proposed RNN model can implement early warning in the fault creep period. The accuracy and reliability of the proposed model are quantitatively verified.

The application on order analysis for the rotating machinery with LabVIEW

Order analysis is regarded as one of the most significant method for monitoring and analyzing rotational machinery for the phenomenon of " frequency smear".However,the order analysis based on resampling is a signal processingwhich converts the constant time interval sampling into constant angle interval sampling,while with the variety of the rotational speed.The superiority of the order analysis is investigatedon implement of order analysis.Andthrough comparing the advantage and disadvantage between spectrum and order analysis,the paper will discuss the order analysis form a deep perspective.