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
In this study,a dynamic model for the bearing rotor system of a high-speed train under variable speed conditions is established.In contrast to previous studies,the contact stress is simplifed in the proposed model and...In this study,a dynamic model for the bearing rotor system of a high-speed train under variable speed conditions is established.In contrast to previous studies,the contact stress is simplifed in the proposed model and the compensation balance excitation caused by the rotor mass eccentricity considered.The angle iteration method is used to overcome the challenge posed by the inability to determine the roller space position during bearing rotation.The simulation results show that the model accurately describes the dynamics of bearings under varying speed profles that contain acceleration,deceleration,and speed oscillation stages.The order ratio spectrum of the bearing vibration signal indicates that both the single and multiple frequencies in the simulation results are consistent with the theoretical results.Experiments on bearings with outer and inner ring faults under various operating conditions are performed to verify the developed model.展开更多
The axial piston pump usually works under variable speed conditions.It is important to evaluate the health status of the axial piston pump under the variable speed condition.Aiming at the characteristic signals obtain...The axial piston pump usually works under variable speed conditions.It is important to evaluate the health status of the axial piston pump under the variable speed condition.Aiming at the characteristic signals obtained under different wear levels of the port plate,a feature signal extraction method under variable speed conditions is proposed.Firstly,the combination of complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)energy spectrum and fast spectral kurtosis principle is used to accurately extract the intrinsic mode function(IMF)component containing the sensitive information of the degraded feature.Then,the aspect ratio analysis method of the angle domain variational mode decomposition(VMD)is used to process the feature index containing the sensitive information of the degraded feature.In order to evaluate the health status of the axial piston pump under variable speed,the vibration reliability analysis method for axial piston pump based on Weibull proportional failure rate model is proposed.The experimental results show that the proposed method can accurately evaluate the health status of the axial piston pump.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
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
In this paper we extend the duality(C_(comp)^(∞)(R^(d))^(BMO(R^d))))^(∗)=H^(1)(R^(d))to generalized Campanato spaces with variable growth condition Lp,φ(R^(d))instead of BMO(R^(d)).We also extend the characterizatio...In this paper we extend the duality(C_(comp)^(∞)(R^(d))^(BMO(R^d))))^(∗)=H^(1)(R^(d))to generalized Campanato spaces with variable growth condition Lp,φ(R^(d))instead of BMO(R^(d)).We also extend the characterization of C_(comp)^(∞)(R^(d))BMO(Rd)by Uchiyama(1978)to C_(comp)^(∞)(R^(d))L _(p,φ)(R^(d)).Moreover,using this characterization,we prove the boundedness of singular and fractional integral operators on C_(comp)^(∞)(R_(d))L_(p,φ)(R^(d)).The function space L_(p,φ)(R^(d))treated in this paper covers the case that it is coincide with Lip_(α) on one area,with BMO on another area and with the Morrey space on the other area,for example.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
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
This paper explores the convergence of a class of optimally conditioned self scaling variable metric (OCSSVM) methods for unconstrained optimization. We show that this class of methods with Wolfe line search are glob...This paper explores the convergence of a class of optimally conditioned self scaling variable metric (OCSSVM) methods for unconstrained optimization. We show that this class of methods with Wolfe line search are globally convergent for general convex functions.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
Numerical simulation are conducted to explore the characteristics of the axial inflow and related aerodynamic noise for a large-scale adjustable fan with the installation angle changing from−12°to 12°.In suc...Numerical simulation are conducted to explore the characteristics of the axial inflow and related aerodynamic noise for a large-scale adjustable fan with the installation angle changing from−12°to 12°.In such a range the maximum static(gauge)pressure at the inlet changes from−2280 Pa to 382 Pa,and the minimum static pressure decreases from−3389 Pa to−8000 Pa.As for the axial intermediate flow surface,one low pressure zone is located at the junction of the suction surface and the hub,another is located at the suction surface close to the casing position.At the outlet boundary,the low pressure is negative and decreases from−1716 Pa to−4589 Pa.The sound pressure level of the inlet and outlet noise tends to increase monotonously by 11.6 dB and 7.3 dB,respectively.The acoustic energy of discrete noise is always higher than that of broadband noise regardless of whether the inlet or outlet flow surfaces are considered.The acoustic energy ratio of discrete noise at the inlet tends to increase from 0.78 to 0.93,while at the outlet it first decreases from 0.79 to 0.73 and then increases to 0.84.展开更多
As the performance of an air-cooled condenser is apt to be affected by the fluctuating ambient condition, some difficulties are brought to the use of a steam feeding water pump in an air-cooled unit. This paper introd...As the performance of an air-cooled condenser is apt to be affected by the fluctuating ambient condition, some difficulties are brought to the use of a steam feeding water pump in an air-cooled unit. This paper introduces a new design of for steam feeding the water pump of an air-cooled unit using the back-pressure steam turbine as the prime motor. Using variable condition analysis on a 600 MW direct air-cooled unit, and with consideration of the effect on the ambient conditions, the feasibility, economy, and adaptability of the design are verified.展开更多
We give the conditionally residual h-integrability with exponent r for an array of random variables and establish the conditional mean convergence of conditionally negatively quadrant dependent and conditionally negat...We give the conditionally residual h-integrability with exponent r for an array of random variables and establish the conditional mean convergence of conditionally negatively quadrant dependent and conditionally negative associated random variables under this integrability. These results generalize and improve the known ones.展开更多
This paper studies the eigenvalue problem for p（x）-Laplacian equations involving Robin boundary condition. We obtain the Euler-Lagrange equation for the minimization of the Rayleigh quotient involving Luxemburg norm...This paper studies the eigenvalue problem for p（x）-Laplacian equations involving Robin boundary condition. We obtain the Euler-Lagrange equation for the minimization of the Rayleigh quotient involving Luxemburg norms in the framework of variable exponent Sobolev space. Using the Ljusternik-Schnirelman principle, for the Robin boundary value problem, we prove the existence of infinitely many eigenvalue sequences and also show that, the smallest eigenvalue exists and is strictly positive, and all eigenfunctions associated with the smallest eigenvalue do not change sign.展开更多
Conditional dependence learning with high-dimensional conditioning variables.Jianxin Bi,Xingdong Feng&Jingyuan Liu.Abstract Conditional dependence plays a crucial role in various statistical procedures,including v...Conditional dependence learning with high-dimensional conditioning variables.Jianxin Bi,Xingdong Feng&Jingyuan Liu.Abstract Conditional dependence plays a crucial role in various statistical procedures,including variable selection,network analysis and causal inference.However,there remains a paucity of relevant research in the context of high-dimensional conditioning variables,a common challenge encountered in the era of big data.To address this issue,many existing studies impose certain model structures,yet high-dimensional conditioning variables often introduce spurious correlations in these models.In this paper,we systematically study the estimation biases inherent in widely-used measures of conditional dependence when spurious variables are present under high-dimensional settings.展开更多
Water tank and channel experiments have been widely utilized in indoor and outdoor airflow studies for their ability to adjust variables and boundary conditions in a controlled environment.This study reviews the exper...Water tank and channel experiments have been widely utilized in indoor and outdoor airflow studies for their ability to adjust variables and boundary conditions in a controlled environment.This study reviews the experimental setup of water tanks and channels,measurement techniques,and their applications in multi-scale airflow modeling,in particular indoor airflow,street-canyon flow,city-scale urban heat circulation and meso-scale boundary-layer flows.Water tanks are mainly adopted for thermally-dominated airflow studies without considering the background wind,while water channels are applied to dynamic-thermal interacting airflow researches.The advantages and disadvantages of commonly adopted measurement techniques in water tank and water channel experiments are summarized in terms of particle image velocimetry(PIV),acoustic Doppler velocimeter(ADV),laser Doppler velocimeter(LDV),and magnetic resonance imaging(MRI).Based on the water tank and channel measurements,the exhaled airflow and natural ventilations are major concerns in indoor environments.Wind-and buoyancy-driven flow,ventilation and pollutant dispersion are investigated within and above the street canyons.The urban heat island circulation and the convective boundary layer are intensively studied under different thermal and stratification conditions.Current limitations include the oversimplification of building models,the inability to ensure strict flow field similarity,and the neglect of specific dimensionless numbers.Water tank and channel experiments serve as important tools for parametric experiments,allowing for the validation of numerical simulations and comparisons with full scale measurements.In the future,water tank and water channel experiments should more closely align with real atmospheric conditions,such as using more realistic physical models,accounting for actual atmospheric background wind circulations,and considering the non-uniform distribution of heat fluxes in urban areas.展开更多
During a high-speed train operation,the train speed changes frequently,resulting in motion change as a function of time.A dynamic model of a double‐row tapered roller bearing system of a high-speed train under variab...During a high-speed train operation,the train speed changes frequently,resulting in motion change as a function of time.A dynamic model of a double‐row tapered roller bearing system of a high-speed train under variable speed conditions is developed.The model takes into consideration the structural characteristics of one outer ring and two inner rings of the train bearing.The angle iteration method is used to determine the rotation angle of the roller within any time period,solving the difficult problem of determining the location of the roller.The outer ring and inner ring faults are captured by the model,and the model response is obtained under variable speed conditions.Experiments are carried out under two fault conditions to validate the model results.The simulation results are found to be in good agreement with the results of the formula,and the errors between the simulation results and the experimental results when the bearing has outer and inner ring faults are found to be,respectively,5.97% and 2.59%,which demonstrates the effectiveness of the model.The influence of outer ring and inner ring faults on system stability is analyzed quantitatively using the Lempel–Ziv complexity.The results show that for low train acceleration,the inner ring fault has a more significant effect on the system stability,while for high acceleration,the outer ring fault has a more significant effect.However,when the train acceleration changes,the outer ring has a greater influence.In practice,train acceleration is usually small and does not frequently change in one operation cycle.Therefore,the inner ring fault of the bearing deserves more attention.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
The inlet attack angle of rotor blade reasonably can be adjusted with the change of the stagger angle of inlet guide vane (IGV); so the efficiency of each condition will be affected. For the purpose to improve the eff...The inlet attack angle of rotor blade reasonably can be adjusted with the change of the stagger angle of inlet guide vane (IGV); so the efficiency of each condition will be affected. For the purpose to improve the efficiency, the DSP (Digital Signal Processor) controller is designed to adjust the stagger angle of IGV automatically in order to optimize the efficiency at any operating condition. The A/D signal collection includes inlet static pressure, outlet static pressure, outlet total pressure, rotor speed and torque signal, the efficiency can be calculated in the DSP, and the angle signal for the stepping motor which control the IGV will be sent out from the D/A. Experimental investigations are performed in a three-stage, low-speed axial compressor with variable inlet guide vanes. It is demonstrated that the DSP designed can well adjust the stagger angle of IGV online, the efficiency under different conditions can be optimized. This establishment of DSP online adjustment scheme may provide a practical solution for improving performance of multi-stage axial flow compressor when its operating condition is varied.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 National Natural Science Foundation of China(Grant Nos.11790282,12032017,11802184,11902205,12002221,11872256)S&T Program of Hebei(Grant No.20310803D)+2 种基金Natural Science Foundation of Hebei Province(Grant No.A2020210028)Postgraduates Innovation Foundation of Hebei Province(Grant No.CXZZBS2019154)State Foundation for Studying Abroad.
文摘In this study,a dynamic model for the bearing rotor system of a high-speed train under variable speed conditions is established.In contrast to previous studies,the contact stress is simplifed in the proposed model and the compensation balance excitation caused by the rotor mass eccentricity considered.The angle iteration method is used to overcome the challenge posed by the inability to determine the roller space position during bearing rotation.The simulation results show that the model accurately describes the dynamics of bearings under varying speed profles that contain acceleration,deceleration,and speed oscillation stages.The order ratio spectrum of the bearing vibration signal indicates that both the single and multiple frequencies in the simulation results are consistent with the theoretical results.Experiments on bearings with outer and inner ring faults under various operating conditions are performed to verify the developed model.
基金Supported by the National Key Research and Development Program of China(No.2019YFB2005204)the National Natural Science Foundation of China(No.52075469,51675461,11673040)+1 种基金the Key Research and Development Program of Hebei Province(No.19273708D)the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems(No.GZKF-201922).
文摘The axial piston pump usually works under variable speed conditions.It is important to evaluate the health status of the axial piston pump under the variable speed condition.Aiming at the characteristic signals obtained under different wear levels of the port plate,a feature signal extraction method under variable speed conditions is proposed.Firstly,the combination of complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)energy spectrum and fast spectral kurtosis principle is used to accurately extract the intrinsic mode function(IMF)component containing the sensitive information of the degraded feature.Then,the aspect ratio analysis method of the angle domain variational mode decomposition(VMD)is used to process the feature index containing the sensitive information of the degraded feature.In order to evaluate the health status of the axial piston pump under variable speed,the vibration reliability analysis method for axial piston pump based on Weibull proportional failure rate model is proposed.The experimental results show that the proposed method can accurately evaluate the health status of the axial piston pump.
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.
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 Grant-in-Aid for Scientific Research(C)(Grant No.21K03304)Japan Society for the Promotion of Sciencethe Research Institute for Mathematical Sciences,an International Joint Usage/Research Center located in Kyoto University。
文摘In this paper we extend the duality(C_(comp)^(∞)(R^(d))^(BMO(R^d))))^(∗)=H^(1)(R^(d))to generalized Campanato spaces with variable growth condition Lp,φ(R^(d))instead of BMO(R^(d)).We also extend the characterization of C_(comp)^(∞)(R^(d))BMO(Rd)by Uchiyama(1978)to C_(comp)^(∞)(R^(d))L _(p,φ)(R^(d)).Moreover,using this characterization,we prove the boundedness of singular and fractional integral operators on C_(comp)^(∞)(R_(d))L_(p,φ)(R^(d)).The function space L_(p,φ)(R^(d))treated in this paper covers the case that it is coincide with Lip_(α) on one area,with BMO on another area and with the Morrey space on the other area,for example.
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This paper explores the convergence of a class of optimally conditioned self scaling variable metric (OCSSVM) methods for unconstrained optimization. We show that this class of methods with Wolfe line search are globally convergent for general convex functions.
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.
基金supported by Key Research and Development Project of Shandong Province[2019GSF109084]Young Scholars Program of Shandong University[2018WLJH73].
文摘Numerical simulation are conducted to explore the characteristics of the axial inflow and related aerodynamic noise for a large-scale adjustable fan with the installation angle changing from−12°to 12°.In such a range the maximum static(gauge)pressure at the inlet changes from−2280 Pa to 382 Pa,and the minimum static pressure decreases from−3389 Pa to−8000 Pa.As for the axial intermediate flow surface,one low pressure zone is located at the junction of the suction surface and the hub,another is located at the suction surface close to the casing position.At the outlet boundary,the low pressure is negative and decreases from−1716 Pa to−4589 Pa.The sound pressure level of the inlet and outlet noise tends to increase monotonously by 11.6 dB and 7.3 dB,respectively.The acoustic energy of discrete noise is always higher than that of broadband noise regardless of whether the inlet or outlet flow surfaces are considered.The acoustic energy ratio of discrete noise at the inlet tends to increase from 0.78 to 0.93,while at the outlet it first decreases from 0.79 to 0.73 and then increases to 0.84.
文摘As the performance of an air-cooled condenser is apt to be affected by the fluctuating ambient condition, some difficulties are brought to the use of a steam feeding water pump in an air-cooled unit. This paper introduces a new design of for steam feeding the water pump of an air-cooled unit using the back-pressure steam turbine as the prime motor. Using variable condition analysis on a 600 MW direct air-cooled unit, and with consideration of the effect on the ambient conditions, the feasibility, economy, and adaptability of the design are verified.
基金Acknowledgements The authors thank Editor Lu and two anonymous referees for their constructive suggestions and comments which helped in significantly improving an earlier version of this paper. This work is supported by the National Natural Science Foundation of China （11171001, 11201001, 11426032）, the Natural Science Foundation of Anhui Province （1308085QA03, 1408085QA02）, the Science Fund for Distinguished Young Scholars of Anhui Province （1508085J06）, and Introduction Projects of Anhui University Academic and Technology Leaders.
文摘We give the conditionally residual h-integrability with exponent r for an array of random variables and establish the conditional mean convergence of conditionally negatively quadrant dependent and conditionally negative associated random variables under this integrability. These results generalize and improve the known ones.
基金Supported by the National Natural Science Foundation of China(Grant No.11571057)
文摘This paper studies the eigenvalue problem for p（x）-Laplacian equations involving Robin boundary condition. We obtain the Euler-Lagrange equation for the minimization of the Rayleigh quotient involving Luxemburg norms in the framework of variable exponent Sobolev space. Using the Ljusternik-Schnirelman principle, for the Robin boundary value problem, we prove the existence of infinitely many eigenvalue sequences and also show that, the smallest eigenvalue exists and is strictly positive, and all eigenfunctions associated with the smallest eigenvalue do not change sign.
文摘Conditional dependence learning with high-dimensional conditioning variables.Jianxin Bi,Xingdong Feng&Jingyuan Liu.Abstract Conditional dependence plays a crucial role in various statistical procedures,including variable selection,network analysis and causal inference.However,there remains a paucity of relevant research in the context of high-dimensional conditioning variables,a common challenge encountered in the era of big data.To address this issue,many existing studies impose certain model structures,yet high-dimensional conditioning variables often introduce spurious correlations in these models.In this paper,we systematically study the estimation biases inherent in widely-used measures of conditional dependence when spurious variables are present under high-dimensional settings.
基金supported by the National Natural Science Foundation of China(No.U2442212,No.42175095,No.42205073&No.42475086)Natural Science Foundation of Guangdong Province(No.2023A1515012863)+1 种基金Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(No.311020001)Fundamental Research Funds for the Central Universities,Sun Yat-sen University(No.24xkjc004).
文摘Water tank and channel experiments have been widely utilized in indoor and outdoor airflow studies for their ability to adjust variables and boundary conditions in a controlled environment.This study reviews the experimental setup of water tanks and channels,measurement techniques,and their applications in multi-scale airflow modeling,in particular indoor airflow,street-canyon flow,city-scale urban heat circulation and meso-scale boundary-layer flows.Water tanks are mainly adopted for thermally-dominated airflow studies without considering the background wind,while water channels are applied to dynamic-thermal interacting airflow researches.The advantages and disadvantages of commonly adopted measurement techniques in water tank and water channel experiments are summarized in terms of particle image velocimetry(PIV),acoustic Doppler velocimeter(ADV),laser Doppler velocimeter(LDV),and magnetic resonance imaging(MRI).Based on the water tank and channel measurements,the exhaled airflow and natural ventilations are major concerns in indoor environments.Wind-and buoyancy-driven flow,ventilation and pollutant dispersion are investigated within and above the street canyons.The urban heat island circulation and the convective boundary layer are intensively studied under different thermal and stratification conditions.Current limitations include the oversimplification of building models,the inability to ensure strict flow field similarity,and the neglect of specific dimensionless numbers.Water tank and channel experiments serve as important tools for parametric experiments,allowing for the validation of numerical simulations and comparisons with full scale measurements.In the future,water tank and water channel experiments should more closely align with real atmospheric conditions,such as using more realistic physical models,accounting for actual atmospheric background wind circulations,and considering the non-uniform distribution of heat fluxes in urban areas.
基金The present work was supported by the National Natural Science Foundation of China (Nos.11790282,12032017,12002221,and 11872256)the National Key R&D Program (2020YFB2007700)+1 种基金the S&T Program of Hebei(20310803D)the Natural Science Foundation of Hebei Province (No.A2020210028).
文摘During a high-speed train operation,the train speed changes frequently,resulting in motion change as a function of time.A dynamic model of a double‐row tapered roller bearing system of a high-speed train under variable speed conditions is developed.The model takes into consideration the structural characteristics of one outer ring and two inner rings of the train bearing.The angle iteration method is used to determine the rotation angle of the roller within any time period,solving the difficult problem of determining the location of the roller.The outer ring and inner ring faults are captured by the model,and the model response is obtained under variable speed conditions.Experiments are carried out under two fault conditions to validate the model results.The simulation results are found to be in good agreement with the results of the formula,and the errors between the simulation results and the experimental results when the bearing has outer and inner ring faults are found to be,respectively,5.97% and 2.59%,which demonstrates the effectiveness of the model.The influence of outer ring and inner ring faults on system stability is analyzed quantitatively using the Lempel–Ziv complexity.The results show that for low train acceleration,the inner ring fault has a more significant effect on the system stability,while for high acceleration,the outer ring fault has a more significant effect.However,when the train acceleration changes,the outer ring has a greater influence.In practice,train acceleration is usually small and does not frequently change in one operation cycle.Therefore,the inner ring fault of the bearing deserves more attention.
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.
基金supported by National Basic Research Program of China 2007CB210104National Natural Science Foundation of China with projects No.51010007.and No.50736007
文摘The inlet attack angle of rotor blade reasonably can be adjusted with the change of the stagger angle of inlet guide vane (IGV); so the efficiency of each condition will be affected. For the purpose to improve the efficiency, the DSP (Digital Signal Processor) controller is designed to adjust the stagger angle of IGV automatically in order to optimize the efficiency at any operating condition. The A/D signal collection includes inlet static pressure, outlet static pressure, outlet total pressure, rotor speed and torque signal, the efficiency can be calculated in the DSP, and the angle signal for the stepping motor which control the IGV will be sent out from the D/A. Experimental investigations are performed in a three-stage, low-speed axial compressor with variable inlet guide vanes. It is demonstrated that the DSP designed can well adjust the stagger angle of IGV online, the efficiency under different conditions can be optimized. This establishment of DSP online adjustment scheme may provide a practical solution for improving performance of multi-stage axial flow compressor when its operating condition is varied.
