Context: Working conditions in the car repair sector are difficult in general. This leads to health risk factors for inexperienced staff. In the bodywork painting workshop, the staff seemed less interested in the risk...Context: Working conditions in the car repair sector are difficult in general. This leads to health risk factors for inexperienced staff. In the bodywork painting workshop, the staff seemed less interested in the risks probably due to negligence or by lack of knowledge. This work aimed to describe the working conditions and their impact on the workers’ health in a workshop of bodywork painting in Conakry. Material and Methods: This was a cross-sectional study over 06 months (from July 01, 2021, to December 31, 2021). Were included the bodybuilders-painters, the painters and the bodybuilders. The data was collected during an interview. We analysed the personal data of the workers, the physical environment factors (lighting, noise, etc.) and, the clinical manifestations felt by the workers. Results: The average age was 37 years extenting from 18 to 54 years and, they were all men. Over 80% of workers were exposed to more than 1000 lux and, 78.2% of workers were exposed to the vibratory intensity level of the cordless drill > 2.5 m/s2. The most frequent symptoms were back pain, headache, itchy eyes, and numbness of fingers and hands. The analysis of working conditions and clinical manifestations showed a significant relationship between the level of illumination and the tingling eyes (p = 0.0007), the vibratory intensity of the drill and the numbness of fingers and hands (p = 0.01). This study revealed that some of the complaints cited are related to the working conditions. Conclusion: Working conditions in a bodywork paint workshop are occupational risk factors that become dangerous if they are unknown. A longitudinal study on the assessment of working conditions could better enlighten us on this phenomenon.展开更多
This paper is concerned with a non-intrusive anomaly detection method for carving machine systems with variant working conditions,and a novel unsupervised detection framework that integrates convolutional autoencoder(...This paper is concerned with a non-intrusive anomaly detection method for carving machine systems with variant working conditions,and a novel unsupervised detection framework that integrates convolutional autoencoder(CAE)and Gaussian mixture hidden Markov model(GMHMM)is proposed.Firstly,the built-in sensor information under normal conditions is recorded,and a 1D convolutional autoencoder is employed to compress high-dimensional time series,thereby transforming the anomaly detection problem in high-dimensional space into a density estimation problem in a latent low-dimensional space.Then,two separate estimation networks are utilized to predict the mixture memberships and state transition probabilities for each sample,enabling GMHMM to handle low-dimensional representations and multi-condition information.Furthermore,a cost function comprising CAE reconstruction and GMHMM probability assessment is constructed for the low-dimensional representation generation and subsequent density estimation in an end-to-end fashion,and the joint optimization effectively enhances the anomaly detection performance.Finally,experiments are carried out on a self-developed multi-axis carving machine platform to validate the effectiveness and superiority of the proposed method.展开更多
The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this p...The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this paper we proposed a diagnostic method for identifying the working condition of the submersible pumping system. Based on analyzing the working principle of the pumping unit and the pump structure, different characteristics in loading and unloading processes of the submersible linear motor were obtained at different working conditions. The characteristic quantities were extracted from operation data of the submersible linear motor. A diagnostic model based on the support vector machine （SVM） method was proposed for identifying the working condition of the submersible pumping unit, where the inputs of the SVM classifier were the characteristic quantities. The performance and the misjudgment rate of this method were analyzed and validated by the data acquired from an experimental simulation platform. The model proposed had an excellent performance in failure diagnosis of the submersible pumping system. The SVM classifier had higher diagnostic accuracy than the learning vector quantization （LVQ） classifier.展开更多
Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have t...Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have time series information.Based on the conventional froth size distribution characteristics,this paper proposes a size trend core feature(STCF)considering the froth size distribution,i.e.,a feature centered on the time series of the froth size distribution.The core features of the trend are extracted,the inter-frame change factor and the inter-frame stability factor are given and two calculation methods of the feature factors are proposed.Meanwhile,the STCF feature algorithm was established based on the core features by adding the inter-frame change factor and the inter-frame stability factor.Finally,a flotation condition recognition model based on BP neural network was established.The experiments show that the recognition model has achieved excellent results,proving that the method proposed effectively overcomes the limitation of the lack of dynamic information in the existing traditional size distribution features and the introduction of the two factors can improve the classification accuracy to varying degrees.展开更多
Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and m...Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.展开更多
The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep le...The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.展开更多
High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump...In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.展开更多
Aiming at solving shield attitude rectification failure problem,a method of shield working condition classification based on support vector data description( SVDD) was introduced. Shield attitude mechanics model conta...Aiming at solving shield attitude rectification failure problem,a method of shield working condition classification based on support vector data description( SVDD) was introduced. Shield attitude mechanics model containing priori knowledge was helpful to feature selection. SVDD handled the one class classification problem and a decision function for attitude rectification was proposed. Experimental results indicate that the method is able to accomplish the shield attitude working condition classification.展开更多
To determine the correlation between the working environment and the health status of employees in solar greenhouse, 1171 employees were surveyed. The results show the ＇Greenhouse diseases＇ are affected by many fact...To determine the correlation between the working environment and the health status of employees in solar greenhouse, 1171 employees were surveyed. The results show the ＇Greenhouse diseases＇ are affected by many factors. Among general uncomforts, the morbidity of the bone and joint damage is the highest and closely related to labor time and age. Planting summer squash and wax gourd more easilv cause skin pruritus.展开更多
The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process.The surface texture feature closely relates to the flotation working conditions and hence can be used ...The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process.The surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform(DTCWT) is proposed for process monitoring of zinc fast roughing.Three-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification(i RFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.展开更多
Based on the FEA software ANSYS,a model was developed to simulate the thermal stress distribution of inert anode.In order to reduce its thermal stress,the effect of some parameters on thermal stress distribution was i...Based on the FEA software ANSYS,a model was developed to simulate the thermal stress distribution of inert anode.In order to reduce its thermal stress,the effect of some parameters on thermal stress distribution was investigated,including the temperature of electrolyte,the current,the anode cathode distance,the anode immersion depth,the surrounding temperature and the convection coefficient between anode and circumstance.The results show that there exists a large axial tensile stress near the tangent interface between the anode and bath,which is the major cause of anode breaking.Increasing the temperature of electrolyte or the anode immersion depth will deteriorate the stress distribution of inert anode.When the bath temperature increases from 750 to 970 ℃,the maximal value and absolute minimal value of the 1st principal stress increase by 29.7% and 29.6%,respectively.When the anode immersion depth is changed from 1 to 10 cm,the maximal value and absolute minimal value of the 1st principal stress increase by 52.1% and 65.0%,respectively.The effects of other parameters on stress distribution are not significant.展开更多
As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the b...As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the bearing and the abnormal vibration of the body.Based on the characteristics of poor lubrication state and complex force of connecting rod small head bearing, a mixed lubrication model considering oil groove feed was established, and the dynamic simulation of the reciprocating compressor model with lubricated bearings was carried out;considering different speeds and gas load conditions, the law of the impact of the eigenvalues changing with working conditions was explored.The fault simulation experiment was carried out by selecting representative working conditions, which verified the correctness of the simulation method.The study found that two contact collisions between the pin and the bearing bush occurred in one cycle, the collision impact was more severe under the wear fault, and the existence of the gap made the dynamic response more sensitive to the change of working conditions.This research provides ideas for the location and feature extraction of fault symptom signal angular segments in the process of complex measured signal processing.展开更多
In this technical note,a novel rating scale(abdominal integral index)was introduced for assessing the conditions of the working laparoscopic space based on linear measurements to select the optimal one or two-stage su...In this technical note,a novel rating scale(abdominal integral index)was introduced for assessing the conditions of the working laparoscopic space based on linear measurements to select the optimal one or two-stage surgical treatment for super-obesity.Patients with the same height and similar BMI values had different rating scale scores,reflecting different conditions of laparoscopic bariatric surgery.The rating scale helps surgeons and patients make a safe option for surgery,depending on the experience of the surgeon and technical laparoscopic conditions.展开更多
A general review is given about the research progress of the rotating machinery condition monitoring under varying working condition. The major typical methods for analyzing are reviewed,including their progress,defic...A general review is given about the research progress of the rotating machinery condition monitoring under varying working condition. The major typical methods for analyzing are reviewed,including their progress,deficiencies and capabilities. Some prospects are given finally.展开更多
Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation a...Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation and development.In this paper,the off-grid wind power hydrogen production system is considered as the research object,and the operating characteristics of a proton exchange membrane(PEM)electrolysis cell,including underload,overload,variable load,and start-stop are analyzed.On this basis,the characteristic extraction of wind power output data after noise reduction is carried out,and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability.The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system.The historical output data of an actual wind farm are used for the case study,and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production.The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems,and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.展开更多
Geomaterials are known to be non-associated materials. Granular soils therefore exhibit a variety of failure modes, with diffuse or localized kinematical patterns. In fact, the notion of failure itself can be confusin...Geomaterials are known to be non-associated materials. Granular soils therefore exhibit a variety of failure modes, with diffuse or localized kinematical patterns. In fact, the notion of failure itself can be confusing with regard to granular soils, because it is not associated with an obvious phenomenology. In this study, we built a proper framework, using the second-order work theory, to describe some failure modes in geomaterials based on energy conservation. The occurrence of failure is defined by an abrupt increase in kinetic energy. The increase in kinetic energy from an equilibrium state, under incremental loading, is shown to be equal to the difference between the external second-order work,involving the external loading parameters, and the internal second-order work, involving the constitutive properties of the material. When a stress limit state is reached, a certain stress component passes through a maximum value and then may decrease. Under such a condition, if a certain additional external loading is applied, the system fails, sharply increasing the strain rate. The internal stress is no longer able to balance the external stress, leading to a dynamic response of the specimen. As an illustration, the theoretical framework was applied to the well-known undrained triaxial test for loose soils. The influence of the loading control mode was clearly highlighted. It is shown that the plastic limit theory appears to be a particular case of this more general second-order work theory. When the plastic limit condition is met, the internal second-order work is nil. A class of incremental external loadings causes the kinetic energy to increase dramatically, leading to the sudden collapse of the specimen, as observed in laboratory.展开更多
The change of working conditions not only makes the data distribution inconsistent,but also increases the diagnosis difficulty of fuzzy samples at the fault boundary.The traditional distance-based deep metric learning...The change of working conditions not only makes the data distribution inconsistent,but also increases the diagnosis difficulty of fuzzy samples at the fault boundary.The traditional distance-based deep metric learning cannot effectively classify the fuzzy samples at the fault boundary.In the traditional transfer learning models,the maximum mean discrepancy(MMD)and joint maximum mean discrepancy only increase the transferability of same-class samples,and neglect the discriminability of different-class samples across different domains.The discriminative joint probability MMD(DJP-MMD)increases the transferability of same-class samples and the discriminability of different-class samples across different domains,but it only considers the global transferability of all fault classes,ignoring the different transferability of each same fault class.Therefore,a Yu norm-based deep transfer metric learning based on weighted DJP-MMD is proposed to further improve the diagnosis accuracy of bearings under variable working conditions.The deep transfer metric learning model adopts the Yu norm-based similarity instead of the distance-based similarity to effectively classify the data samples,especially those at the fault boundary,and uses the weighted DJP-MMD to measure the data distribution discrepancy between the source and target domains to increase the transferability of each same-class samples and discriminability of different-class samples across different domains.Through the fault diagnosis analysis on bearings under variable working conditions,the diagnosis results demonstrate that the proposed deep transfer metric learning model can diagnose bearing faults with higher accuracy,stronger generalization and anti-noise capabilities compared with other fault diagnosis methods based on transfer learning.展开更多
The wind environment serves as an important indicator of the comfort level of the human settlement environment.A favorable wind environment in courtyards can offer residents a better living and activity environment.Ba...The wind environment serves as an important indicator of the comfort level of the human settlement environment.A favorable wind environment in courtyards can offer residents a better living and activity environment.Based on the Climatic Suitability Evaluating on Human Settlement,this study employs Computational Fluid Dynamics(CFD)simulation analysis to evaluate the wind environment comfort in rural courtyards,and elucidates the interactions between various factors influencing the courtyard wind environment and the courtyard wind environment.It is anticipated that the findings of this study will serve as a valuable reference for the renovation and new construction of rural houses,with a focus on optimizing the wind environment.展开更多
文摘Context: Working conditions in the car repair sector are difficult in general. This leads to health risk factors for inexperienced staff. In the bodywork painting workshop, the staff seemed less interested in the risks probably due to negligence or by lack of knowledge. This work aimed to describe the working conditions and their impact on the workers’ health in a workshop of bodywork painting in Conakry. Material and Methods: This was a cross-sectional study over 06 months (from July 01, 2021, to December 31, 2021). Were included the bodybuilders-painters, the painters and the bodybuilders. The data was collected during an interview. We analysed the personal data of the workers, the physical environment factors (lighting, noise, etc.) and, the clinical manifestations felt by the workers. Results: The average age was 37 years extenting from 18 to 54 years and, they were all men. Over 80% of workers were exposed to more than 1000 lux and, 78.2% of workers were exposed to the vibratory intensity level of the cordless drill > 2.5 m/s2. The most frequent symptoms were back pain, headache, itchy eyes, and numbness of fingers and hands. The analysis of working conditions and clinical manifestations showed a significant relationship between the level of illumination and the tingling eyes (p = 0.0007), the vibratory intensity of the drill and the numbness of fingers and hands (p = 0.01). This study revealed that some of the complaints cited are related to the working conditions. Conclusion: Working conditions in a bodywork paint workshop are occupational risk factors that become dangerous if they are unknown. A longitudinal study on the assessment of working conditions could better enlighten us on this phenomenon.
基金Supported by the National Natural Science Foundation of China(No.62203390).
文摘This paper is concerned with a non-intrusive anomaly detection method for carving machine systems with variant working conditions,and a novel unsupervised detection framework that integrates convolutional autoencoder(CAE)and Gaussian mixture hidden Markov model(GMHMM)is proposed.Firstly,the built-in sensor information under normal conditions is recorded,and a 1D convolutional autoencoder is employed to compress high-dimensional time series,thereby transforming the anomaly detection problem in high-dimensional space into a density estimation problem in a latent low-dimensional space.Then,two separate estimation networks are utilized to predict the mixture memberships and state transition probabilities for each sample,enabling GMHMM to handle low-dimensional representations and multi-condition information.Furthermore,a cost function comprising CAE reconstruction and GMHMM probability assessment is constructed for the low-dimensional representation generation and subsequent density estimation in an end-to-end fashion,and the joint optimization effectively enhances the anomaly detection performance.Finally,experiments are carried out on a self-developed multi-axis carving machine platform to validate the effectiveness and superiority of the proposed method.
文摘The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this paper we proposed a diagnostic method for identifying the working condition of the submersible pumping system. Based on analyzing the working principle of the pumping unit and the pump structure, different characteristics in loading and unloading processes of the submersible linear motor were obtained at different working conditions. The characteristic quantities were extracted from operation data of the submersible linear motor. A diagnostic model based on the support vector machine （SVM） method was proposed for identifying the working condition of the submersible pumping unit, where the inputs of the SVM classifier were the characteristic quantities. The performance and the misjudgment rate of this method were analyzed and validated by the data acquired from an experimental simulation platform. The model proposed had an excellent performance in failure diagnosis of the submersible pumping system. The SVM classifier had higher diagnostic accuracy than the learning vector quantization （LVQ） classifier.
基金Project(U1701261)supported by the National Science Foundation of China,Guangdong Joint Fund of Key ProjectsProject(61771492)supported by the National Natural Science Foundation of ChinaProject(2018GK4016)supported by Hunan Province Strategic Emerging Industry Science and Technology Research and Major Science and Technology Achievement Transformation Project,China。
文摘Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have time series information.Based on the conventional froth size distribution characteristics,this paper proposes a size trend core feature(STCF)considering the froth size distribution,i.e.,a feature centered on the time series of the froth size distribution.The core features of the trend are extracted,the inter-frame change factor and the inter-frame stability factor are given and two calculation methods of the feature factors are proposed.Meanwhile,the STCF feature algorithm was established based on the core features by adding the inter-frame change factor and the inter-frame stability factor.Finally,a flotation condition recognition model based on BP neural network was established.The experiments show that the recognition model has achieved excellent results,proving that the method proposed effectively overcomes the limitation of the lack of dynamic information in the existing traditional size distribution features and the introduction of the two factors can improve the classification accuracy to varying degrees.
基金Supported by National Natural Science Foundation of China(Grant No.51835009).
文摘Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.
基金supported in part by the National Natural Science Foundation of China under Grant U1908212,62203432 and 92067205in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03 and 2023-Z15in part by the Natural Science Foundation of Liaoning Province under Grant 2020-KF-11-02.
文摘The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
文摘In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.
基金National Basic Research Program of China ( 973 Program) ( No. 2007CB714006)
文摘Aiming at solving shield attitude rectification failure problem,a method of shield working condition classification based on support vector data description( SVDD) was introduced. Shield attitude mechanics model containing priori knowledge was helpful to feature selection. SVDD handled the one class classification problem and a decision function for attitude rectification was proposed. Experimental results indicate that the method is able to accomplish the shield attitude working condition classification.
基金supported by the Profession Expert Group of Facility Cultivation and Engineering(CARS-25-D-03)
文摘To determine the correlation between the working environment and the health status of employees in solar greenhouse, 1171 employees were surveyed. The results show the ＇Greenhouse diseases＇ are affected by many factors. Among general uncomforts, the morbidity of the bone and joint damage is the highest and closely related to labor time and age. Planting summer squash and wax gourd more easilv cause skin pruritus.
文摘The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process.The surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform(DTCWT) is proposed for process monitoring of zinc fast roughing.Three-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification(i RFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.
基金Project (2005CB623703) supported by the National Basic Research and Development Program of ChinaProject (50474051) supported by the National Natural Science Foundation of China
文摘Based on the FEA software ANSYS,a model was developed to simulate the thermal stress distribution of inert anode.In order to reduce its thermal stress,the effect of some parameters on thermal stress distribution was investigated,including the temperature of electrolyte,the current,the anode cathode distance,the anode immersion depth,the surrounding temperature and the convection coefficient between anode and circumstance.The results show that there exists a large axial tensile stress near the tangent interface between the anode and bath,which is the major cause of anode breaking.Increasing the temperature of electrolyte or the anode immersion depth will deteriorate the stress distribution of inert anode.When the bath temperature increases from 750 to 970 ℃,the maximal value and absolute minimal value of the 1st principal stress increase by 29.7% and 29.6%,respectively.When the anode immersion depth is changed from 1 to 10 cm,the maximal value and absolute minimal value of the 1st principal stress increase by 52.1% and 65.0%,respectively.The effects of other parameters on stress distribution are not significant.
基金Supported by the National Natural Science Foundation of China (No.52101343)。
文摘As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the bearing and the abnormal vibration of the body.Based on the characteristics of poor lubrication state and complex force of connecting rod small head bearing, a mixed lubrication model considering oil groove feed was established, and the dynamic simulation of the reciprocating compressor model with lubricated bearings was carried out;considering different speeds and gas load conditions, the law of the impact of the eigenvalues changing with working conditions was explored.The fault simulation experiment was carried out by selecting representative working conditions, which verified the correctness of the simulation method.The study found that two contact collisions between the pin and the bearing bush occurred in one cycle, the collision impact was more severe under the wear fault, and the existence of the gap made the dynamic response more sensitive to the change of working conditions.This research provides ideas for the location and feature extraction of fault symptom signal angular segments in the process of complex measured signal processing.
文摘In this technical note,a novel rating scale(abdominal integral index)was introduced for assessing the conditions of the working laparoscopic space based on linear measurements to select the optimal one or two-stage surgical treatment for super-obesity.Patients with the same height and similar BMI values had different rating scale scores,reflecting different conditions of laparoscopic bariatric surgery.The rating scale helps surgeons and patients make a safe option for surgery,depending on the experience of the surgeon and technical laparoscopic conditions.
基金National Natural Science Foundation of China(No.51125022)the Ph.D.Programs Foundation of Ministry of Education of China(No.20110201110025)Fundamental Research Funds for the Central Universities of China
文摘A general review is given about the research progress of the rotating machinery condition monitoring under varying working condition. The major typical methods for analyzing are reviewed,including their progress,deficiencies and capabilities. Some prospects are given finally.
基金supported by the National Key Research and Development Program of China(Program Number 2021YFB4000100)the Beijing Postdoctoral Research Foundation(Grant Number 2023-ZZ-63).
文摘Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation and development.In this paper,the off-grid wind power hydrogen production system is considered as the research object,and the operating characteristics of a proton exchange membrane(PEM)electrolysis cell,including underload,overload,variable load,and start-stop are analyzed.On this basis,the characteristic extraction of wind power output data after noise reduction is carried out,and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability.The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system.The historical output data of an actual wind farm are used for the case study,and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production.The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems,and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.
基金the French Research Network Me Ge (Multiscale and Multiphysics Couplings in Geo-environmental Mechanics GDR CNRS 3176/2340, 2008e2015) for having supported this work
文摘Geomaterials are known to be non-associated materials. Granular soils therefore exhibit a variety of failure modes, with diffuse or localized kinematical patterns. In fact, the notion of failure itself can be confusing with regard to granular soils, because it is not associated with an obvious phenomenology. In this study, we built a proper framework, using the second-order work theory, to describe some failure modes in geomaterials based on energy conservation. The occurrence of failure is defined by an abrupt increase in kinetic energy. The increase in kinetic energy from an equilibrium state, under incremental loading, is shown to be equal to the difference between the external second-order work,involving the external loading parameters, and the internal second-order work, involving the constitutive properties of the material. When a stress limit state is reached, a certain stress component passes through a maximum value and then may decrease. Under such a condition, if a certain additional external loading is applied, the system fails, sharply increasing the strain rate. The internal stress is no longer able to balance the external stress, leading to a dynamic response of the specimen. As an illustration, the theoretical framework was applied to the well-known undrained triaxial test for loose soils. The influence of the loading control mode was clearly highlighted. It is shown that the plastic limit theory appears to be a particular case of this more general second-order work theory. When the plastic limit condition is met, the internal second-order work is nil. A class of incremental external loadings causes the kinetic energy to increase dramatically, leading to the sudden collapse of the specimen, as observed in laboratory.
基金funded by the National Natural Science Foundation of China(Grant No.51775391).
文摘The change of working conditions not only makes the data distribution inconsistent,but also increases the diagnosis difficulty of fuzzy samples at the fault boundary.The traditional distance-based deep metric learning cannot effectively classify the fuzzy samples at the fault boundary.In the traditional transfer learning models,the maximum mean discrepancy(MMD)and joint maximum mean discrepancy only increase the transferability of same-class samples,and neglect the discriminability of different-class samples across different domains.The discriminative joint probability MMD(DJP-MMD)increases the transferability of same-class samples and the discriminability of different-class samples across different domains,but it only considers the global transferability of all fault classes,ignoring the different transferability of each same fault class.Therefore,a Yu norm-based deep transfer metric learning based on weighted DJP-MMD is proposed to further improve the diagnosis accuracy of bearings under variable working conditions.The deep transfer metric learning model adopts the Yu norm-based similarity instead of the distance-based similarity to effectively classify the data samples,especially those at the fault boundary,and uses the weighted DJP-MMD to measure the data distribution discrepancy between the source and target domains to increase the transferability of each same-class samples and discriminability of different-class samples across different domains.Through the fault diagnosis analysis on bearings under variable working conditions,the diagnosis results demonstrate that the proposed deep transfer metric learning model can diagnose bearing faults with higher accuracy,stronger generalization and anti-noise capabilities compared with other fault diagnosis methods based on transfer learning.
文摘The wind environment serves as an important indicator of the comfort level of the human settlement environment.A favorable wind environment in courtyards can offer residents a better living and activity environment.Based on the Climatic Suitability Evaluating on Human Settlement,this study employs Computational Fluid Dynamics(CFD)simulation analysis to evaluate the wind environment comfort in rural courtyards,and elucidates the interactions between various factors influencing the courtyard wind environment and the courtyard wind environment.It is anticipated that the findings of this study will serve as a valuable reference for the renovation and new construction of rural houses,with a focus on optimizing the wind environment.
