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.

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.

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.

DEVELOPMENT OF VIRTUAL INSTRUMENT IN CHARACTERISTIC ANALYSIS OF ROTATING MACHINERY BASED ON INSTANTANEOUS FREQUENCY ESTIMATION

Based on the recently quick-developing time-frequency analysis (TFA)technique and virtual instrument (VI) technique, a virtual instrument in characteristic analysis ofrotating machinery is researched and developed successfully. By utilizing instantaneous frequencyestimation (IFE) theoretics of TFA technique, and based on IFE of peak searching on thetime-frequency spectrum, order analysis (OA) functions is put forward and implemented, such as orderspectrum, order spectrum matrix, order tracking, order tracking filtering, and order componentextraction, etc. Unlike the home and abroad existing popular characteristic analyzers, which needkey phasing devices such as shaft encoder, phase-locked loop (PLL), phase-locked multiple frequency,tachometer, etc, to implement constant angle sampling directly or indirectly, whereas thisinstrument only uses the vibration signal of rotating machinery to carry out OA. This instrumentmakes up the shortage of these traditional instruments in analyzing the non-stationary signal ofrun-up and run-down process of rotating machinery. Therefore, it is a great breakthrough for theexisting order analyzers.

A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

Synthesis of the fluid machinery network in a circulating water system

Energy consumption of the fluid machinery network in a circulating water system takes up a large part of energy consumption in the process industry, so optimization on the network will enhance the economic and environmental performance of the industry. In this paper, a synthesis approach is proposed to obtain the optimal network structure. The effective height curves are used as tools to perform energy analysis, so that the potential placement of water turbines and auxiliary pumps can be determined with energy benefit. Then economic optimization is carried out, by the mathematical model with the total cost as the objective function, to identify the branches for water turbines and auxiliary pumps with economic benefit. In this way, the optimal fluid machinery network structure can be obtained. The results of case study indicate that the proposed synthesis approach to optimize the fluid machinery network will obtain more remarkable benefits on economy, compared to optimizing only the water turbine network or pump network. The results under different flowrates of circulating water reveal that using a water turbine to recover power or adding an auxiliary pump to save energy in branches are only suitable to the flowrate in a certain range.

Development of Centrifugal Atomization Technique in the Field of Plant Protection Machinery

Centrifugal atomization technique is internationally recognized as one of the most appropriate methods for obtaining uniform fine droplets, and also an advanced technology to get a narrower range of droplet particle spectrum. The importance and related mechanism of centrifugal atomization technique in the field of plant protection are expounded, and the overview of centrifugal atomization technique at home and abroad is introduced. Moreover, the direction of future research in this field is prospected.

Effect of different machinery and rolling times on the microbial activity of reclamation soil in coal area

Mechanical construction will put influence on the biological characters of reclaimed soils,as well as the soil quality.In order to explore the changing rule of soil microbial quantity and respiratory capacity under different construction machineries and rolling times,and find the optimal processing conditions,an experiment was set up and a simulation experimental area was chosen,in which we simulated the main types of reclamation in coal mine area.After 2 years’natural aging,we collected surface soil samples(0-20 cm)that can be used for experimental analysis.The result shows that changing rules of soil biological factors are different with different construction machineries,and soil properties are closest to the normal soil when adopting the combination of“crawler dozer×5 compaction times”and“dump truck×3 compaction times”,which shows that the soil quality is better under this condition.