The counterflow burner is a combustion device used for research on combustion.By utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitate...The counterflow burner is a combustion device used for research on combustion.By utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitates the optimization of the combustion process and enhances combustion efficiency.Among existing deep convolutional models,InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt.It has garnered significant attention for its computational efficiency,remarkable model accuracy,and exceptional feature extraction capabilities.However,since this model still has limitations in the combustion state recognition task,we propose a Triple-Scale Multi-Stage InceptionNeXt(TSMS-InceptionNeXt)combustion state recognitionmethod based on feature extraction optimization.First,to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images,we introduce Triplet Attention,which applies attention to the width,height,and Red Green Blue(RGB)dimensions of the flame images to enhance its ability to model dynamic features.Secondly,to address the issue of key information loss in the Inception deep convolution layers,we propose a Similarity-based Feature Concentration(SimC)mechanism to enhance the model’s capability to concentrate on critical features.Next,to address the insufficient receptive field of the model,we propose a Multi-Scale Dilated Channel Parallel Integration(MDCPI)mechanism to enhance the model’s ability to extract multi-scale contextual information.Finally,to address the issue of the model’s Multi-Layer Perceptron Head(MlpHead)neglecting channel interactions,we propose a Channel Shuffle-Guided Channel-Spatial Attention(ShuffleCS)mechanism,which integrates information from different channels to further enhance the representational power of the input features.To validate the effectiveness of the method,experiments are conducted on the counterflow burner flame visible light image dataset.The experimental results show that the TSMS-InceptionNeXt model achieved an accuracy of 85.71%on the dataset,improving by 2.38%over the baseline model and outperforming the baseline model’s performance.It achieved accuracy improvements of 10.47%,4.76%,11.19%,and 9.28%compared to the Reparameterized Visual Geometry Group(RepVGG),Squeeze-erunhanced Axial Transoformer(SeaFormer),Simplified Graph Transformers(SGFormer),and VanillaNet models,respectively,effectively enhancing the recognition performance for combustion states in counterflow burners.展开更多
The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the s...The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.展开更多
Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, d...Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.展开更多
An operating condition recognition approach of wind turbine spindle is proposed based on supervisory control and data acquisition(SCADA)normal data drive.Firstly,the SCADA raw data of wind turbine under full working c...An operating condition recognition approach of wind turbine spindle is proposed based on supervisory control and data acquisition(SCADA)normal data drive.Firstly,the SCADA raw data of wind turbine under full working conditions are cleaned and feature extracted.Then the spindle speed is employed as the output parameter,and the single and combined normal behavior model of the wind turbine spindle is constructed sequentially with the preprocessed data,with the evaluation indexes selected as the optimal model.Finally,calculating the spindle operation status index according to the slidingwindowprinciple,ascertaining the threshold value for identifying the abnormal spindle operation status by the hypothesis of small probability event,analyzing the 2.5 MW wind turbine SCADA data froma domestic wind field as a sample,The results show that the fault warning time of the early warningmodel is 5.7 h ahead of the actual fault occurrence time,as well as the identification and early warning of abnormal wind turbine spindle operationwithout abnormal data or a priori knowledge of related faults.展开更多
The thermal-based imaging technique has recently attracted the attention of researchers who are interested in the recognition of human affects dueto its ability to measure the facial transient temperature, which is co...The thermal-based imaging technique has recently attracted the attention of researchers who are interested in the recognition of human affects dueto its ability to measure the facial transient temperature, which is correlated withhuman affects and robustness against illumination changes. Therefore, studieshave increasingly used the thermal imaging as a potential and supplemental solution to overcome the challenges of visual (RGB) imaging, such as the variation oflight conditions and revealing original human affect. Moreover, the thermal-basedimaging has shown promising results in the detection of psychophysiological signals, such as pulse rate and respiration rate in a contactless and noninvasive way.This paper presents a brief review on human affects and focuses on the advantages and challenges of the thermal imaging technique. In addition, this paper discusses the stages of thermal-based human affective state recognition, such asdataset type, preprocessing stage, region of interest (ROI), feature descriptors,and classification approaches with a brief performance analysis based on a number of works in the literature. This analysis could help beginners in the thermalimaging and affective recognition domain to explore numerous approaches usedby researchers to construct an affective state system based on thermal imaging.展开更多
A new method of state recognition based on the theory of evidence was proposed. By this method, the plausible function which the sample awaiting recognition belongs to each category can be obtained through distance fu...A new method of state recognition based on the theory of evidence was proposed. By this method, the plausible function which the sample awaiting recognition belongs to each category can be obtained through distance function. For the marginal samples,two or a batch of evidences can be combined and a new plausible function can be obtained by new evidence. Then the categories of samples can be determined according to plausibility function. This method provides a beder reasoning framework. The result proves the rate of recoghition correctness.展开更多
The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stag...The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stage.Effectively identifying and predicting the smelt-ing stage poses a significant challenge within industrial production.Traditional image-based methodologies,which rely on a single static flame image as input,demonstrate low recognition accuracy and inadequately extract the dynamic changes in smelting stage.To address this issue,the present study introduces an innovative recognition model that preprocesses flame video sequences from the furnace mouth and then employs a convolutional recurrent neural network(CRNN)to extract spatiotemporal features and derive recognition outputs.Ad-ditionally,we adopt feature layer visualization techniques to verify the model’s effectiveness and further enhance model performance by integrating the Bayesian optimization algorithm.The results indicate that the ResNet18 with convolutional block attention module(CBAM)in the convolutional layer demonstrates superior image feature extraction capabilities,achieving an accuracy of 90.70%and an area under the curve of 98.05%.The constructed Bayesian optimization-CRNN(BO-CRNN)model exhibits a significant improvement in comprehensive performance,with an accuracy of 97.01%and an area under the curve of 99.85%.Furthermore,statistics on the model’s average recognition time,computational complexity,and parameter quantity(Average recognition time:5.49 ms,floating-point opera-tions per second:18260.21 M(1 M=1×10^(6)),parameters:11.58 M)demonstrate superior performance.Through extensive repeated ex-periments on real-world datasets,the proposed CRNN model is capable of rapidly and accurately identifying smelting stages,offering a novel approach for converter smelting endpoint control.展开更多
Spinal disease is an important cause of cervical discomfort,low back pain,radiating pain in the limbs,and neurogenic intermittent claudication,and its incidence is increasing annually.From the etiological viewpoint,th...Spinal disease is an important cause of cervical discomfort,low back pain,radiating pain in the limbs,and neurogenic intermittent claudication,and its incidence is increasing annually.From the etiological viewpoint,these symptoms are directly caused by the compression of the spinal cord,nerve roots,and blood vessels and are most effectively treated with surgery.Spinal surgeries are primarily performed using two different techniques:spinal canal decompression and internal fixation.In the past,tactile sensation was the primary method used by surgeons to understand the state of the tissue within the operating area.However,this method has several disadvantages because of its subjectivity.Therefore,it has become the focus of spinal surgery research so as to strengthen the objectivity of tissue state recognition,improve the accuracy of safe area location,and avoid surgical injury to tissues.Aside from traditional imaging methods,surgical sensing techniques based on force,bioelectrical impedance,and other methods have been gradually developed and tested in the clinical setting.This article reviews the progress of different tissue state recognition methods in spinal surgery and summarizes their advantages and disadvantages.展开更多
Based on the fuzzy characteristic of the pulse state and syndromes differentiation thinking mode of TCM, an information fusing recognition method of pulse states based on SFNN (Stochastic Fuzzy Neural Network) is pres...Based on the fuzzy characteristic of the pulse state and syndromes differentiation thinking mode of TCM, an information fusing recognition method of pulse states based on SFNN (Stochastic Fuzzy Neural Network) is presented in this paper. With the learning ability in parameters and structure, SFNN fuses the measurement information of three pulse-state sensors distributed in Cun, Guan, and Chi location of body for the pulse state recognition. The experimental results show that the percentage of correct recognition with new method is higher than that by single-data recognition one, with fewer off-line train numbers.展开更多
The Pattem Recognition Laboratory, set up byin 1984 and ratified as a state key lab in 1987, isattached to the CAS Institute of Automation (IA). The Laboraory’s founding director was Profes-sor Ma Songde, now the dir...The Pattem Recognition Laboratory, set up byin 1984 and ratified as a state key lab in 1987, isattached to the CAS Institute of Automation (IA). The Laboraory’s founding director was Profes-sor Ma Songde, now the director of the Institute ofAntomation. Its current director is Professor TanTieniu.展开更多
Drilling motors are widely used in unconventional oil and gas exploration.Due to the increased non-productive time and drilling costs brought about by accidental damage to drilling motors,predictive maintenance for dr...Drilling motors are widely used in unconventional oil and gas exploration.Due to the increased non-productive time and drilling costs brought about by accidental damage to drilling motors,predictive maintenance for drilling motors is necessary to optimize asset utilization.However,service companies face significant challenges in achieving predictive maintenance:operational data acquisition,automated statistics analysis,and drilling state recognition.This paper presents a miniature vibration recorder,an automatic statistical analysis method,and a layered recognition algorithm to resolve these challenges and improve tool maintenance efficiency.The designed recorder can be installed in the catch of a conventional mud motor to record drilling dynamics over a drilling motor's entire operation cycle.Time-series data from the recorder can be used to automatically generate operation statistics,mitigating the costs incurred by manual data analysis.The layered recognition algorithm then enables the automatic identification of drilling operation states,i.e.,surface,downhole non-drilling,downhole sliding,and downhole rotation.The solutions were validated by deploying the recorder in drilling field runs and analyzing recorded data using the associated design software,yielding a functional data collection,automatic data statistical analysis,and operation state recognition accuracy of 95%.Through achieving improved data collection and analysis,the recorder and software introduced in this work can notify motor owners of the detailed operation history of their tools and enable informed preventive maintenance.展开更多
In late September,a number of Western countries including Britain,Canada,Australia,France,Portugal,Luxembourg,Malta and Monaco announced their formal recognition of the State of Palestine.
Traveling by high-speed rail and railway transportation have become an important part of people’s life and social production.Track is the basic equipment of railway transportation,and its performance directly affects...Traveling by high-speed rail and railway transportation have become an important part of people’s life and social production.Track is the basic equipment of railway transportation,and its performance directly affects the service lifetime of railway lines and vehicles.The anomaly detection of rail fasteners is in a priority,while the traditional manual method is extremely inefficient and dangerous to workers.Therefore,this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners,but also to recognize the fasteners states.To be more specific,this paper mainly studies the rail fastener detection based on improved You can Only Look Once version 5(YOLOv5)network,and completes the real-time classification of fastener states.The improved YOLOv5 network proposed contains five sections,which are Input,Backbone,Neck,Head Detector and a read-only Few-shot Example Learning module.The main purpose of this project is to improve the detection precision and shorten the detection time.Ultimately,the rail fastener detection system proposed in this paper is confirmed to be superior to other advanced algorithms.This model achieves on-line fastener detection by completing the“sampling-detection-recognition-warning”cycle of a single sample before the next image is sampled.Specifically,the mean average precision of model reaches 94.6%.And the model proposed reaches the speed of 12 ms per image in the deployment environment of NVIDIA GTX1080Ti GPU.展开更多
MDM (minimum distance method) is a very popular algorithm in staterecognition. But it has a presupposition, that is, the distance within one class must be shorterenough than the distance between classes. When this pre...MDM (minimum distance method) is a very popular algorithm in staterecognition. But it has a presupposition, that is, the distance within one class must be shorterenough than the distance between classes. When this presupposition is not satisfied, the method isno longer valid. In order to overcome the shortcomings of MDM, an improved minimum distance method(IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstratethat IMDM has two advantages, that is, the rate of recognition is faster and the accuracy ofrecognition is higher compared with MDM.展开更多
文摘The counterflow burner is a combustion device used for research on combustion.By utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitates the optimization of the combustion process and enhances combustion efficiency.Among existing deep convolutional models,InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt.It has garnered significant attention for its computational efficiency,remarkable model accuracy,and exceptional feature extraction capabilities.However,since this model still has limitations in the combustion state recognition task,we propose a Triple-Scale Multi-Stage InceptionNeXt(TSMS-InceptionNeXt)combustion state recognitionmethod based on feature extraction optimization.First,to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images,we introduce Triplet Attention,which applies attention to the width,height,and Red Green Blue(RGB)dimensions of the flame images to enhance its ability to model dynamic features.Secondly,to address the issue of key information loss in the Inception deep convolution layers,we propose a Similarity-based Feature Concentration(SimC)mechanism to enhance the model’s capability to concentrate on critical features.Next,to address the insufficient receptive field of the model,we propose a Multi-Scale Dilated Channel Parallel Integration(MDCPI)mechanism to enhance the model’s ability to extract multi-scale contextual information.Finally,to address the issue of the model’s Multi-Layer Perceptron Head(MlpHead)neglecting channel interactions,we propose a Channel Shuffle-Guided Channel-Spatial Attention(ShuffleCS)mechanism,which integrates information from different channels to further enhance the representational power of the input features.To validate the effectiveness of the method,experiments are conducted on the counterflow burner flame visible light image dataset.The experimental results show that the TSMS-InceptionNeXt model achieved an accuracy of 85.71%on the dataset,improving by 2.38%over the baseline model and outperforming the baseline model’s performance.It achieved accuracy improvements of 10.47%,4.76%,11.19%,and 9.28%compared to the Reparameterized Visual Geometry Group(RepVGG),Squeeze-erunhanced Axial Transoformer(SeaFormer),Simplified Graph Transformers(SGFormer),and VanillaNet models,respectively,effectively enhancing the recognition performance for combustion states in counterflow burners.
基金the National Key Research and Development Program of China(No.2020YFB1713500)the Natural Science Basic Research Program of Shaanxi(Grant No.2023JCYB289)+1 种基金the National Natural Science Foundation of China(Grant No.52175112)the Fundamental Research Funds for the Central Universities(Grant No.ZYTS23102).
文摘The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.
文摘Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.
基金supported by the National Natural Science Foundation of China(No.51965034).
文摘An operating condition recognition approach of wind turbine spindle is proposed based on supervisory control and data acquisition(SCADA)normal data drive.Firstly,the SCADA raw data of wind turbine under full working conditions are cleaned and feature extracted.Then the spindle speed is employed as the output parameter,and the single and combined normal behavior model of the wind turbine spindle is constructed sequentially with the preprocessed data,with the evaluation indexes selected as the optimal model.Finally,calculating the spindle operation status index according to the slidingwindowprinciple,ascertaining the threshold value for identifying the abnormal spindle operation status by the hypothesis of small probability event,analyzing the 2.5 MW wind turbine SCADA data froma domestic wind field as a sample,The results show that the fault warning time of the early warningmodel is 5.7 h ahead of the actual fault occurrence time,as well as the identification and early warning of abnormal wind turbine spindle operationwithout abnormal data or a priori knowledge of related faults.
基金funded by the research university grant by Universiti Sains Malaysia[1001.PKOMP.8014001].
文摘The thermal-based imaging technique has recently attracted the attention of researchers who are interested in the recognition of human affects dueto its ability to measure the facial transient temperature, which is correlated withhuman affects and robustness against illumination changes. Therefore, studieshave increasingly used the thermal imaging as a potential and supplemental solution to overcome the challenges of visual (RGB) imaging, such as the variation oflight conditions and revealing original human affect. Moreover, the thermal-basedimaging has shown promising results in the detection of psychophysiological signals, such as pulse rate and respiration rate in a contactless and noninvasive way.This paper presents a brief review on human affects and focuses on the advantages and challenges of the thermal imaging technique. In addition, this paper discusses the stages of thermal-based human affective state recognition, such asdataset type, preprocessing stage, region of interest (ROI), feature descriptors,and classification approaches with a brief performance analysis based on a number of works in the literature. This analysis could help beginners in the thermalimaging and affective recognition domain to explore numerous approaches usedby researchers to construct an affective state system based on thermal imaging.
文摘A new method of state recognition based on the theory of evidence was proposed. By this method, the plausible function which the sample awaiting recognition belongs to each category can be obtained through distance function. For the marginal samples,two or a batch of evidences can be combined and a new plausible function can be obtained by new evidence. Then the categories of samples can be determined according to plausibility function. This method provides a beder reasoning framework. The result proves the rate of recoghition correctness.
基金financially supported by the National Natural Science Foundation of China(No.52374320).
文摘The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stage.Effectively identifying and predicting the smelt-ing stage poses a significant challenge within industrial production.Traditional image-based methodologies,which rely on a single static flame image as input,demonstrate low recognition accuracy and inadequately extract the dynamic changes in smelting stage.To address this issue,the present study introduces an innovative recognition model that preprocesses flame video sequences from the furnace mouth and then employs a convolutional recurrent neural network(CRNN)to extract spatiotemporal features and derive recognition outputs.Ad-ditionally,we adopt feature layer visualization techniques to verify the model’s effectiveness and further enhance model performance by integrating the Bayesian optimization algorithm.The results indicate that the ResNet18 with convolutional block attention module(CBAM)in the convolutional layer demonstrates superior image feature extraction capabilities,achieving an accuracy of 90.70%and an area under the curve of 98.05%.The constructed Bayesian optimization-CRNN(BO-CRNN)model exhibits a significant improvement in comprehensive performance,with an accuracy of 97.01%and an area under the curve of 99.85%.Furthermore,statistics on the model’s average recognition time,computational complexity,and parameter quantity(Average recognition time:5.49 ms,floating-point opera-tions per second:18260.21 M(1 M=1×10^(6)),parameters:11.58 M)demonstrate superior performance.Through extensive repeated ex-periments on real-world datasets,the proposed CRNN model is capable of rapidly and accurately identifying smelting stages,offering a novel approach for converter smelting endpoint control.
基金This work was supported by the Beijing Natural Science Foundation(No.LI 82068)。
文摘Spinal disease is an important cause of cervical discomfort,low back pain,radiating pain in the limbs,and neurogenic intermittent claudication,and its incidence is increasing annually.From the etiological viewpoint,these symptoms are directly caused by the compression of the spinal cord,nerve roots,and blood vessels and are most effectively treated with surgery.Spinal surgeries are primarily performed using two different techniques:spinal canal decompression and internal fixation.In the past,tactile sensation was the primary method used by surgeons to understand the state of the tissue within the operating area.However,this method has several disadvantages because of its subjectivity.Therefore,it has become the focus of spinal surgery research so as to strengthen the objectivity of tissue state recognition,improve the accuracy of safe area location,and avoid surgical injury to tissues.Aside from traditional imaging methods,surgical sensing techniques based on force,bioelectrical impedance,and other methods have been gradually developed and tested in the clinical setting.This article reviews the progress of different tissue state recognition methods in spinal surgery and summarizes their advantages and disadvantages.
文摘Based on the fuzzy characteristic of the pulse state and syndromes differentiation thinking mode of TCM, an information fusing recognition method of pulse states based on SFNN (Stochastic Fuzzy Neural Network) is presented in this paper. With the learning ability in parameters and structure, SFNN fuses the measurement information of three pulse-state sensors distributed in Cun, Guan, and Chi location of body for the pulse state recognition. The experimental results show that the percentage of correct recognition with new method is higher than that by single-data recognition one, with fewer off-line train numbers.
文摘The Pattem Recognition Laboratory, set up byin 1984 and ratified as a state key lab in 1987, isattached to the CAS Institute of Automation (IA). The Laboraory’s founding director was Profes-sor Ma Songde, now the director of the Institute ofAntomation. Its current director is Professor TanTieniu.
基金funded by the National Science Foundation of China (U20B2029)China Key Research and Development Program (2023YFC2810900)+5 种基金Natural Science Basic Research Plan in Shaanxi Province (2023-JC-QN-0405)General Project of Shaanxi Province's Key Research and Development Plan (2024GX-YBXM-504)Shaanxi Province Technical Innovation Guidance Special Project (2024ZCYYDP-22)Shaanxi QinChuangYuan ‘Scientist + Engineer’ Team Construction Plan (2022kxj-125)Shaanxi Universities’ Young Scholar Innovation TeamXi’an Shiyou University’s Innovation Team
文摘Drilling motors are widely used in unconventional oil and gas exploration.Due to the increased non-productive time and drilling costs brought about by accidental damage to drilling motors,predictive maintenance for drilling motors is necessary to optimize asset utilization.However,service companies face significant challenges in achieving predictive maintenance:operational data acquisition,automated statistics analysis,and drilling state recognition.This paper presents a miniature vibration recorder,an automatic statistical analysis method,and a layered recognition algorithm to resolve these challenges and improve tool maintenance efficiency.The designed recorder can be installed in the catch of a conventional mud motor to record drilling dynamics over a drilling motor's entire operation cycle.Time-series data from the recorder can be used to automatically generate operation statistics,mitigating the costs incurred by manual data analysis.The layered recognition algorithm then enables the automatic identification of drilling operation states,i.e.,surface,downhole non-drilling,downhole sliding,and downhole rotation.The solutions were validated by deploying the recorder in drilling field runs and analyzing recorded data using the associated design software,yielding a functional data collection,automatic data statistical analysis,and operation state recognition accuracy of 95%.Through achieving improved data collection and analysis,the recorder and software introduced in this work can notify motor owners of the detailed operation history of their tools and enable informed preventive maintenance.
文摘In late September,a number of Western countries including Britain,Canada,Australia,France,Portugal,Luxembourg,Malta and Monaco announced their formal recognition of the State of Palestine.
基金This work was supported by the National Natural Science Foundation of China(61871046,SM,http://www.nsfc.gov.cn/).
文摘Traveling by high-speed rail and railway transportation have become an important part of people’s life and social production.Track is the basic equipment of railway transportation,and its performance directly affects the service lifetime of railway lines and vehicles.The anomaly detection of rail fasteners is in a priority,while the traditional manual method is extremely inefficient and dangerous to workers.Therefore,this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners,but also to recognize the fasteners states.To be more specific,this paper mainly studies the rail fastener detection based on improved You can Only Look Once version 5(YOLOv5)network,and completes the real-time classification of fastener states.The improved YOLOv5 network proposed contains five sections,which are Input,Backbone,Neck,Head Detector and a read-only Few-shot Example Learning module.The main purpose of this project is to improve the detection precision and shorten the detection time.Ultimately,the rail fastener detection system proposed in this paper is confirmed to be superior to other advanced algorithms.This model achieves on-line fastener detection by completing the“sampling-detection-recognition-warning”cycle of a single sample before the next image is sampled.Specifically,the mean average precision of model reaches 94.6%.And the model proposed reaches the speed of 12 ms per image in the deployment environment of NVIDIA GTX1080Ti GPU.
基金This work was financially supported by the National Natural Science Foundation of China under the contract No.69372031.]
文摘MDM (minimum distance method) is a very popular algorithm in staterecognition. But it has a presupposition, that is, the distance within one class must be shorterenough than the distance between classes. When this presupposition is not satisfied, the method isno longer valid. In order to overcome the shortcomings of MDM, an improved minimum distance method(IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstratethat IMDM has two advantages, that is, the rate of recognition is faster and the accuracy ofrecognition is higher compared with MDM.
