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.

Compound Fault Diagnosis for Rotating Machinery:State-of-the-Art,Challenges,and Opportunities

Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

A Multi-mode Electronic Load Sensing Control Scheme with Power Limitation and Pressure Cut-off for Mobile Machinery

In mobile machinery,hydro-mechanical pumps are increasingly replaced by electronically controlled pumps to improve the automation level,but diversified control functions(e.g.,power limitation and pressure cut-off)are integrated into the electronic controller only from the pump level,leading to the potential instability of the overall system.To solve this problem,a multi-mode electrohydraulic load sensing(MELS)control scheme is proposed especially considering the switching stability from the system level,which includes four working modes of flow control,load sensing,power limitation,and pressure control.Depending on the actual working requirements,the switching rules for the different modes and the switching direction(i.e.,the modes can be switched bilaterally or unilaterally)are defined.The priority of different modes is also defined,from high to low:pressure control,power limitation,load sensing,and flow control.When multiple switching rules are satisfied at the same time,the system switches to the control mode with the highest priority.In addition,the switching stability between flow control and pressure control modes is analyzed,and the controller parameters that guarantee the switching stability are obtained.A comparative study is carried out based on a test rig with a 2-ton hydraulic excavator.The results show that the MELS controller can achieve the control functions of proper flow supplement,power limitation,and pressure cut-off,which has good stability performance when switching between different control modes.This research proposes the MELS control method that realizes the stability of multi-mode switching of the hydraulic system of mobile machinery under different working conditions.

Cavitation Diagnostics Based on Self-Tuning VMD for Fluid Machinery with Low-SNR Conditions

Variational mode decomposition(VMD)is a suitable tool for processing cavitation-induced vibration signals and is greatly affected by two parameters:the decomposed number K and penalty factorαunder strong noise interference.To solve this issue,this study proposed self-tuning VMD(SVMD)for cavitation diagnostics in fluid machinery,with a special focus on low signal-to-noise ratio conditions.A two-stage progressive refinement of the coarsely located target penalty factor for SVMD was conducted to narrow down the search space for accelerated decomposition.A hybrid optimized sparrow search algorithm(HOSSA)was developed for optimalαfine-tuning in a refined space based on fault-type-guided objective functions.Based on the submodes obtained using exclusive penalty factors in each iteration,the cavitation-related characteristic frequencies(CCFs)were extracted for diagnostics.The power spectrum correlation coefficient between the SVMD reconstruction and original signals was employed as a stop criterion to determine whether to stop further decomposition.The proposed SVMD overcomes the blindness of setting the mode number K in advance and the drawback of sharing penalty factors for all submodes in fixed-parameter and parameter-optimized VMDs.Comparisons with other existing methods in simulation signal decomposition and in-lab experimental data demonstrated the advantages of the proposed method in accurately extracting CCFs with lower computational cost.SVMD especially enhances the denoising capability of the VMD-based method.

Using Digital Twin to Diagnose Faults in Braiding Machinery Based on IoT

The digital twin(DT)includes real-time data analytics based on the actual product or manufacturing processing parameters.Data from digital twins can predict asset maintenance requirements ahead of time.This saves money by decreasing operating expenses and asset downtime,which improves company efficiency.In this paper,a digital twin in braiding machinery based on IoT(DTBM-IoT)used to diagnose faults.When an imbalance fault occurs,the system gathers experimental data.After that,the information is sent into a digital win model of the rotor system to see whether it can quantify and locate imbalance for defect detection.It is possible to anticipate asset maintenance requirements with DT technology by IoT(Internet of Things)sensors,XR(X-Ray)capabilities,and AI-powered analytics.A DT model’s appropriate design and flexibility remain difficult because of the nonlinear dynamics and unpre-dictability inherent in the degrading process of equipment.The results indicate that the DT in braiding machinery developed allows for precise diagnostic and dynamic deterioration analysis.At least there is 37%growth in efficiency over conventional approaches.

Nonfarm employment, agricultural machinery service purchase and agricultural production efficiency:An empirical study based on China's major wheat-producing areas

Based on the cross-sectional data of the survey conducted in China's main wheat producing areas at the end of 2019,this paper uses the translog production function model to estimate agricultural technical efficiency,and studies the relationship between nonfarm employment,agricultural machinery service purchase and agricultural production efficiency.The results show that the household non-agricultural employment ratio and non-agricultural income have a significant positive impact on the purchase of agricultural machinery services.In addition to the effect of scale efficiency,non-agricultural employment does not have a significant impact on agricultural technical efficiency,and labor transfer does not have a significant negative impact on agricultural production.

Jingwei textile machinery:A variety of core equipment appeared at the exhibition

Intellectualization and sustainable development are still the mainstream trends of textile industry.ITMA 2023,a four-year textile machinery event,arrived as scheduled and to be held in Milan,Italy from June 8 to 14.This grand meeting will certainly give a strong impetus to"Transfroming the World of Textiles".

ACIMIT forecasts show an increase in global demand for textile machinery from mid-2023--Interview with Mr.Alex Zucchi,President of ACIMIT,Association of Italian Textile Machinery Manufacturers

For the past year, according to preliminary data in 2022, the value of Italian machinery production is expected to exceed 2.6 billion euro, up about 11% on the previous year. Exports account for more than 87% of this value. Despite the negative factors(inflationary pressures and political crises caused by the pandemic and Russian-Ukrainian war) even in 2022 the upward trend of our industry goes on.

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.

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.

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.

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.