This study explores the aerodynamic performance and flow field characteristics of supercritical carbon dioxide(sCO_(2))centrifugal compressors under varying operating conditions.In particular,the Sandia main compresso...This study explores the aerodynamic performance and flow field characteristics of supercritical carbon dioxide(sCO_(2))centrifugal compressors under varying operating conditions.In particular,the Sandia main compressor impeller model is used as a reference system.Through three-dimensional numerical simulations,we examine the Mach number distribution,temperature field,blade pressure pulsation spectra,and velocity field evolution,and identify accordingly the operating boundaries ensuring stability and the mechanisms responsible for performance degradation.Findings indicate a stable operating range for mass flow rate between 0.74 and 3.74 kg/s.At the lower limit(0.74 kg/s),the maximum Mach number within the compressor decreases by 28%,while the temperature gradient sharpens,entropy rises notably,and fluid density varies significantly.The maximum pressure near the blades increases by 6%,yet flow velocity near the blades and outlet declines,with a 19%reduction in peak speed.Consequently,isentropic efficiency falls by 13%.Conversely,at 3.74 kg/s,the maximum Mach number increases by 23.7%,with diminished temperature gradients and minor fluid density variations.However,insufficient enthalpy gain and intensified pressure pulsations near the blades result in a 12%pressure drop.Peak velocity within the impeller channel surges by 23%,amplifying velocity gradients,inducing flow separation,and ultimately reducing the pressure ratio from 1.47 to 1.34.展开更多
In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption...In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption that machinery operates under a constant rotational speed. However, when the rotational speed varies in the broader range, the pass-frequencies vary with the change of rotational speed and bearing faults cannot be identified by the interval of impacts. Researches related to automatic diagnosis for rotational machinery in variable operating conditions were quite few. A novel automatic feature extraction method is proposed based on a pseudo-Wigner-Ville distribution （PWVD） and an extraction of symptom parameter （SP）. An extraction method for instantaneous feature spectrum is presented using the relative crossing information （RCI） and sequential inference approach, by which the feature spectrum from time-frequency distribution can be automatically, sequentially extracted. The SPs are considered in the frequency domain using the extracted feature spectrum to identify among the conditions of a machine. A method to obtain the synthetic symptom parameter is also proposed by the least squares mapping （LSM） technique for increasing the diagnosis sensitivity of SP. Practical examples of diagnosis for bearings are given in order to verify the effectiveness of the proposed method. The verification results show that the features of bearing faults, such as the outer-race, inner-race and roller element defects have been effectively extracted, and the proposed method can be used for condition diagnosis of a machine under the variable rotational speed.展开更多
Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variabl...Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variable service conditions, and their diagnostic environments are complex and dynamic. In the process of bearing diagnosis, fault datasets are relatively scarce compared with datasets representing normal operating conditions. These challenges frequently cause the practicality of fault detection to decline, the extraction of fault features to be incomplete, and the diagnostic accuracy of many existing models to decrease. In this work, a transfer-learning framework, designated DSCNN-HA-TL, is introduced to address the enduring challenge of cross-condition diagnosis in rolling-bearing fault detection. The framework integrates a window global mixed attention mechanism with a deep separable convolutional network, thereby enabling adaptation to fault detection tasks under diverse operating conditions. First, a Convolutional Neural Network (CNN) is employed as the foundational architecture, where the original convolutional layers are enhanced through the incorporation of depthwise separable convolutions, resulting in a Depthwise Separable Convolutional Neural Network (DSCNN) architecture. Subsequently, the extraction of fault characteristics is further refined through a dual-branch network that integrates hybrid attention mechanisms, specifically windowed and global attention mechanisms. This approach enables the acquisition of multi-level feature fusion information, thereby enhancing the accuracy of fault classification. The integration of these features not only optimizes the characteristic extraction process but also yields improvements in accuracy, representational capacity, and robustness in fault feature recognition. In conclusion, the proposed method achieved average precisions of 99.93% and 99.55% in transfer learning tasks, as demonstrated by the experimental results obtained from the CWRU public dataset and the bearing fault detection platform dataset. The experimental findings further provided a detailed comparison between the diagnostic models before and after the enhancement, thereby substantiating the pronounced advantages of the DSCNN-HA-TL approach in accurately identifying faults in critical mechanical components under diverse operating conditions.展开更多
In the field of fault diagnosis for rolling bearings under variable working conditions,significant progress has been made using methods based on unsupervised domain adaptation(UDA).However,most existing UDA methods pr...In the field of fault diagnosis for rolling bearings under variable working conditions,significant progress has been made using methods based on unsupervised domain adaptation(UDA).However,most existing UDA methods primarily achieve identification by directly aligning the distributions of the source and target domains,often overlooking the relevance of samples between different domains,which may result in incomplete extraction of deep features and alignment of feature distributions.Therefore,this study proposes a novel domain adaptation network based on Gaussian prior distributions,aiming at solving the challenges of cross working conditions bearing fault diagnosis.The method consists of a feature mining module and an adversarial domain adaptation module.The former effectively extracts deep features by stacking multiple residual networks(Resnet),while the latter employs an indirect latent alignment strategy,using Gaussian prior distributions in the latent feature space to indirectly align the feature distributions of the source and target domains,achieving more precise feature alignment.In addition,an adaptive factor is introduced to dynamically assess the method’s transfer and discriminative capabilities.Experimental data from two bearing systems validate that the method can effectively transfer source domain knowledge to the target domain,confirming its effectiveness.展开更多
Accelerating the process of intelligent manufacturing and the demand for new industrial productivity,the operating conditions of machinery and equipment have become ever more severe.As an important link to ensure the ...Accelerating the process of intelligent manufacturing and the demand for new industrial productivity,the operating conditions of machinery and equipment have become ever more severe.As an important link to ensure the stable operation of the production process,the condition monitoring and fault diagnosis of equipment have become equally important.The fault diagnosis of equipment in actual production is often challenged by variable working conditions,large differences in data distribution,and lack of labeled samples,etc.Traditional fault diagnosis methods are often difficult to achieve ideal results in these complex environments.Transfer learning(TL)as an emerging technology can effectively utilize existing knowledge and data to improve the diagnostic performance.Firstly,this paper analyzes the trend of mechanical equipment fault diagnosis and explains the basic concept of TL.Then TL based on parameters,TL based on features,TL based on instances and domain adaptive(DA)methods are summarized and analyzed in terms of existing TL methods.Finally,the problems faced in the current TL research are summarized and the future development trend is pointed out.This review aims to help researchers in related fields understand the latest progress of TL and promote the application and development of TL in mechanical equipment diagnosis.展开更多
Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supp...Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.展开更多
基金National Science Foundation of China(grant numbers 52366009 and 52130607)Doble First-Class Key Programof Gansu Provincial Department of Education(grant number GCJ2022-38)+1 种基金2022 Gansu Provincial University Industry Support Plan Project(grant number 2022CYZC-21)KeyR&DProgramofGansu Province of China(grant number 22YF7GA163).
文摘This study explores the aerodynamic performance and flow field characteristics of supercritical carbon dioxide(sCO_(2))centrifugal compressors under varying operating conditions.In particular,the Sandia main compressor impeller model is used as a reference system.Through three-dimensional numerical simulations,we examine the Mach number distribution,temperature field,blade pressure pulsation spectra,and velocity field evolution,and identify accordingly the operating boundaries ensuring stability and the mechanisms responsible for performance degradation.Findings indicate a stable operating range for mass flow rate between 0.74 and 3.74 kg/s.At the lower limit(0.74 kg/s),the maximum Mach number within the compressor decreases by 28%,while the temperature gradient sharpens,entropy rises notably,and fluid density varies significantly.The maximum pressure near the blades increases by 6%,yet flow velocity near the blades and outlet declines,with a 19%reduction in peak speed.Consequently,isentropic efficiency falls by 13%.Conversely,at 3.74 kg/s,the maximum Mach number increases by 23.7%,with diminished temperature gradients and minor fluid density variations.However,insufficient enthalpy gain and intensified pressure pulsations near the blades result in a 12%pressure drop.Peak velocity within the impeller channel surges by 23%,amplifying velocity gradients,inducing flow separation,and ultimately reducing the pressure ratio from 1.47 to 1.34.
基金supported by National Natural Science Foundation of China (Grant No. 50875016, 51075023)Fundamental Research Funds for the Central Universities of China (Grant No. JD0903, JD0904)
文摘In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption that machinery operates under a constant rotational speed. However, when the rotational speed varies in the broader range, the pass-frequencies vary with the change of rotational speed and bearing faults cannot be identified by the interval of impacts. Researches related to automatic diagnosis for rotational machinery in variable operating conditions were quite few. A novel automatic feature extraction method is proposed based on a pseudo-Wigner-Ville distribution （PWVD） and an extraction of symptom parameter （SP）. An extraction method for instantaneous feature spectrum is presented using the relative crossing information （RCI） and sequential inference approach, by which the feature spectrum from time-frequency distribution can be automatically, sequentially extracted. The SPs are considered in the frequency domain using the extracted feature spectrum to identify among the conditions of a machine. A method to obtain the synthetic symptom parameter is also proposed by the least squares mapping （LSM） technique for increasing the diagnosis sensitivity of SP. Practical examples of diagnosis for bearings are given in order to verify the effectiveness of the proposed method. The verification results show that the features of bearing faults, such as the outer-race, inner-race and roller element defects have been effectively extracted, and the proposed method can be used for condition diagnosis of a machine under the variable rotational speed.
基金supported by the National Natural Science Foundation of China(12272259)the Key Research and Development Fund of Universities in Hebei Province(2510800601A).
文摘Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variable service conditions, and their diagnostic environments are complex and dynamic. In the process of bearing diagnosis, fault datasets are relatively scarce compared with datasets representing normal operating conditions. These challenges frequently cause the practicality of fault detection to decline, the extraction of fault features to be incomplete, and the diagnostic accuracy of many existing models to decrease. In this work, a transfer-learning framework, designated DSCNN-HA-TL, is introduced to address the enduring challenge of cross-condition diagnosis in rolling-bearing fault detection. The framework integrates a window global mixed attention mechanism with a deep separable convolutional network, thereby enabling adaptation to fault detection tasks under diverse operating conditions. First, a Convolutional Neural Network (CNN) is employed as the foundational architecture, where the original convolutional layers are enhanced through the incorporation of depthwise separable convolutions, resulting in a Depthwise Separable Convolutional Neural Network (DSCNN) architecture. Subsequently, the extraction of fault characteristics is further refined through a dual-branch network that integrates hybrid attention mechanisms, specifically windowed and global attention mechanisms. This approach enables the acquisition of multi-level feature fusion information, thereby enhancing the accuracy of fault classification. The integration of these features not only optimizes the characteristic extraction process but also yields improvements in accuracy, representational capacity, and robustness in fault feature recognition. In conclusion, the proposed method achieved average precisions of 99.93% and 99.55% in transfer learning tasks, as demonstrated by the experimental results obtained from the CWRU public dataset and the bearing fault detection platform dataset. The experimental findings further provided a detailed comparison between the diagnostic models before and after the enhancement, thereby substantiating the pronounced advantages of the DSCNN-HA-TL approach in accurately identifying faults in critical mechanical components under diverse operating conditions.
文摘In the field of fault diagnosis for rolling bearings under variable working conditions,significant progress has been made using methods based on unsupervised domain adaptation(UDA).However,most existing UDA methods primarily achieve identification by directly aligning the distributions of the source and target domains,often overlooking the relevance of samples between different domains,which may result in incomplete extraction of deep features and alignment of feature distributions.Therefore,this study proposes a novel domain adaptation network based on Gaussian prior distributions,aiming at solving the challenges of cross working conditions bearing fault diagnosis.The method consists of a feature mining module and an adversarial domain adaptation module.The former effectively extracts deep features by stacking multiple residual networks(Resnet),while the latter employs an indirect latent alignment strategy,using Gaussian prior distributions in the latent feature space to indirectly align the feature distributions of the source and target domains,achieving more precise feature alignment.In addition,an adaptive factor is introduced to dynamically assess the method’s transfer and discriminative capabilities.Experimental data from two bearing systems validate that the method can effectively transfer source domain knowledge to the target domain,confirming its effectiveness.
基金National Natural Science Foundation of China(52065030)Key Scientific Research Projects of Yunnan Province(202202AC080008).
文摘Accelerating the process of intelligent manufacturing and the demand for new industrial productivity,the operating conditions of machinery and equipment have become ever more severe.As an important link to ensure the stable operation of the production process,the condition monitoring and fault diagnosis of equipment have become equally important.The fault diagnosis of equipment in actual production is often challenged by variable working conditions,large differences in data distribution,and lack of labeled samples,etc.Traditional fault diagnosis methods are often difficult to achieve ideal results in these complex environments.Transfer learning(TL)as an emerging technology can effectively utilize existing knowledge and data to improve the diagnostic performance.Firstly,this paper analyzes the trend of mechanical equipment fault diagnosis and explains the basic concept of TL.Then TL based on parameters,TL based on features,TL based on instances and domain adaptive(DA)methods are summarized and analyzed in terms of existing TL methods.Finally,the problems faced in the current TL research are summarized and the future development trend is pointed out.This review aims to help researchers in related fields understand the latest progress of TL and promote the application and development of TL in mechanical equipment diagnosis.
基金supported by National Key R&D Program of China(Grant No.2023YFE0108600)National Natural Science Foundation of China(Grant No.51806190)+1 种基金National Key R&D Program of China(Grant No.2022YFB3304502)Self-directed project,State Key Laboratory of Clean Energy Utilization.
文摘Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.
