Inter-shaft bearing is a crucial supporting component of a dual rotor structure aviation engine.Its structural and operational characteristics make it prone to frequent and highly hazardous faults,which can easily lea...Inter-shaft bearing is a crucial supporting component of a dual rotor structure aviation engine.Its structural and operational characteristics make it prone to frequent and highly hazardous faults,which can easily lead to catastrophic accidents such as the failure of the entire rotor system.Therefore,it is significant to realize vibration fault monitoring and warning of inter-shaft bearings.Aero-engine vibration has complex characteristics,with diagnostic methods facing major limitations.Firstly,the compact engine structure and inter-shaft bearing placement cause weak,attenuated fault signals with significant interference,complicating feature extraction.Secondly,in counter-rotating dual-rotor systems,inter-shaft bearing components rotate oppositely,producing higher characteristic frequencies than co-rotating bearings.High-frequency fault signals often overlap or appear harmonic,and their propagation is easily affected by structural complexities,making accurate monitoring challenging.To address these challenges,a method combining Traversal Index Enhanced-gram(TIEgram)and autocorrelation(AC)is proposed for extracting weak fault features in inter-shaft bearings.TIEgram selects the optimal frequency band for resonance demodulation,isolating fault-related signal components.To counter nonstationary signals from aero-engine dynamics,slip-ratio domain order tracking transforms time-domain signals into angular-domain stationary signals.Autocorrelation analysis then yields the squared envelope autocorrelation spectrum,compared with bearing fault characteristic orders for diagnosis.Simulation and experimental results demonstrate the method's effectiveness in extracting weak inter-shaft bearing fault features.展开更多
When the bi-stable stochastic resonance method was applied to enhance weak thruster fault for autonomous underwater vehicle(AUV), the enhancement performance could not satisfy the detection requirement of weak thruste...When the bi-stable stochastic resonance method was applied to enhance weak thruster fault for autonomous underwater vehicle(AUV), the enhancement performance could not satisfy the detection requirement of weak thruster fault. As for this problem, a fault feature enhancement method based on mono-stable stochastic resonance was proposed. In the method, in order to improve the enhancement performance of weak thruster fault feature, the conventional bi-stable potential function was changed to mono-stable potential function which was more suitable for aperiodic signals. Furthermore, when particle swarm optimization was adopted to adjust the parameters of mono-stable stochastic resonance system, the global convergent time would be long. An improved particle swarm optimization method was developed by changing the linear inertial weighted function as nonlinear function with cosine function, so as to reduce the global convergent time. In addition, when the conventional wavelet reconstruction method was adopted to detect the weak thruster fault, undetected fault or false alarm may occur. In order to successfully detect the weak thruster fault, a weak thruster detection method was proposed based on the integration of stochastic resonance and wavelet reconstruction. In the method, the optimal reconstruction scale was determined by comparing wavelet entropies corresponding to each decomposition scale. Finally, pool-experiments were performed on AUV with thruster fault. The effectiveness of the proposed mono-stable stochastic resonance method in enhancing fault feature and reducing the global convergent time was demonstrated in comparison with particle swarm optimization based bi-stochastic resonance method. Furthermore, the effectiveness of the proposed fault detection method was illustrated in comparison with the conventional wavelet reconstruction.展开更多
On the basis of the Xining active urban fault survey, we studied the relationship between the active urban fault and fold deformation. The result of this research shows that the Huangshuihe fault and the NW-striking f...On the basis of the Xining active urban fault survey, we studied the relationship between the active urban fault and fold deformation. The result of this research shows that the Huangshuihe fault and the NW-striking fault on the northern bank of the Huangshulbe River are tensional faults on top of an anticline, the Nanchuanhe fault is a transverse tear fault resulting from differential folding on two sides of the fault, the east bank of the Beichuanhe River fault is a compressional fault developed on the core or climb of a syncline. By balance profile analysis of fold deformation and inversion of gravity anomaly data, we obtained the depth of the detachment plane and established the seismotectonic model of the ）fining urban area. Based on the seismotectonic model, we analyzed the earthquake potential of the active urban fault.展开更多
Stochastic resonance can use noise to enhance weak signals,effectively reducing the effect of noise signals on feature extraction.In order to improve the early fault recognition rate of rolling bearings,and to overcom...Stochastic resonance can use noise to enhance weak signals,effectively reducing the effect of noise signals on feature extraction.In order to improve the early fault recognition rate of rolling bearings,and to overcome the shortcomings of lack of interaction in the selection of SR(Stochastic Resonance)method parameters and the lack of validation of the extracted features,an adaptive genetic random resonance early fault diagnosis method for rolling bearings was proposed.compared with the existing methods,the AGSR(Adaptive Genetic Stochastic Resonance)method uses genetic algorithms to optimize the system parameters,and further optimizes the parameters while considering the interaction between the parameters.This method can effectively extract the weak fault features of the bearing.In order to verify the effect of feature extraction,the feature signal extracted by AGSR method was input into the Fully connected neural network for fault diagnosis.the practicality of the algorithm is verified by simulation data and rolling bearing experimental data.the results show that the proposed method can effectively detect the early weak features of rolling bearings,and the fault diagnosis effect is better than the existing methods.展开更多
Aiming at the fact that the rotor winding inter-turn weak faults can hardly be detected due to the strong electromagnetic coupling effect in the excitation system,an interval observer based on current residual is desi...Aiming at the fact that the rotor winding inter-turn weak faults can hardly be detected due to the strong electromagnetic coupling effect in the excitation system,an interval observer based on current residual is designed.Firstly,the mechanism of the inter-turn short circuit of the rotor winding in the excitation system is modeled under the premise of stable working conditions,and electromagnetic decoupling and system simplification are carried out through Park Transform.An interval observer is designed based on the current residual in the two-phase coordinate system,and the sensitive and stable conditions of the observer is preset.The fault diagnosis process based on the interval observer is formulated,and the observer gain matrix is convexly optimized by linear matrix inequality.The numerical simulation and experimental results show that the inter-turn short circuit weak fault is hardly detected directly through the current signal,but the fault is quickly and accurately diagnosed through the residual internal observer.Compared with the traditional fault diagnosis method based on excitation current,the diagnosis speed and accuracy are greatly improved,and the probability of misdiagnosis also decreases.This method provides a theoretical basis for weak fault identification of excitation systems,and is of great significance for the operation and maintenance of excitation systems.展开更多
基金Supported by the Joint Fund of the Ministry of Education of China(Grant No.8091B022203)Youth Talent Support Project(Grant No.2022-JCJQ-QT-059).
文摘Inter-shaft bearing is a crucial supporting component of a dual rotor structure aviation engine.Its structural and operational characteristics make it prone to frequent and highly hazardous faults,which can easily lead to catastrophic accidents such as the failure of the entire rotor system.Therefore,it is significant to realize vibration fault monitoring and warning of inter-shaft bearings.Aero-engine vibration has complex characteristics,with diagnostic methods facing major limitations.Firstly,the compact engine structure and inter-shaft bearing placement cause weak,attenuated fault signals with significant interference,complicating feature extraction.Secondly,in counter-rotating dual-rotor systems,inter-shaft bearing components rotate oppositely,producing higher characteristic frequencies than co-rotating bearings.High-frequency fault signals often overlap or appear harmonic,and their propagation is easily affected by structural complexities,making accurate monitoring challenging.To address these challenges,a method combining Traversal Index Enhanced-gram(TIEgram)and autocorrelation(AC)is proposed for extracting weak fault features in inter-shaft bearings.TIEgram selects the optimal frequency band for resonance demodulation,isolating fault-related signal components.To counter nonstationary signals from aero-engine dynamics,slip-ratio domain order tracking transforms time-domain signals into angular-domain stationary signals.Autocorrelation analysis then yields the squared envelope autocorrelation spectrum,compared with bearing fault characteristic orders for diagnosis.Simulation and experimental results demonstrate the method's effectiveness in extracting weak inter-shaft bearing fault features.
基金Project(51279040)supported by the National Natural Science Foundation of China
文摘When the bi-stable stochastic resonance method was applied to enhance weak thruster fault for autonomous underwater vehicle(AUV), the enhancement performance could not satisfy the detection requirement of weak thruster fault. As for this problem, a fault feature enhancement method based on mono-stable stochastic resonance was proposed. In the method, in order to improve the enhancement performance of weak thruster fault feature, the conventional bi-stable potential function was changed to mono-stable potential function which was more suitable for aperiodic signals. Furthermore, when particle swarm optimization was adopted to adjust the parameters of mono-stable stochastic resonance system, the global convergent time would be long. An improved particle swarm optimization method was developed by changing the linear inertial weighted function as nonlinear function with cosine function, so as to reduce the global convergent time. In addition, when the conventional wavelet reconstruction method was adopted to detect the weak thruster fault, undetected fault or false alarm may occur. In order to successfully detect the weak thruster fault, a weak thruster detection method was proposed based on the integration of stochastic resonance and wavelet reconstruction. In the method, the optimal reconstruction scale was determined by comparing wavelet entropies corresponding to each decomposition scale. Finally, pool-experiments were performed on AUV with thruster fault. The effectiveness of the proposed mono-stable stochastic resonance method in enhancing fault feature and reducing the global convergent time was demonstrated in comparison with particle swarm optimization based bi-stochastic resonance method. Furthermore, the effectiveness of the proposed fault detection method was illustrated in comparison with the conventional wavelet reconstruction.
基金This project was sponsored by National Development and Reform Commission (NDRC) on studies of experimental exploration of active fault in urban area(20041138)
文摘On the basis of the Xining active urban fault survey, we studied the relationship between the active urban fault and fold deformation. The result of this research shows that the Huangshuihe fault and the NW-striking fault on the northern bank of the Huangshulbe River are tensional faults on top of an anticline, the Nanchuanhe fault is a transverse tear fault resulting from differential folding on two sides of the fault, the east bank of the Beichuanhe River fault is a compressional fault developed on the core or climb of a syncline. By balance profile analysis of fold deformation and inversion of gravity anomaly data, we obtained the depth of the detachment plane and established the seismotectonic model of the ）fining urban area. Based on the seismotectonic model, we analyzed the earthquake potential of the active urban fault.
基金The authors would like to acknowledge the financial support from the National Science Foundation of China(Grant Nos.51505234,51575283,51405241).
文摘Stochastic resonance can use noise to enhance weak signals,effectively reducing the effect of noise signals on feature extraction.In order to improve the early fault recognition rate of rolling bearings,and to overcome the shortcomings of lack of interaction in the selection of SR(Stochastic Resonance)method parameters and the lack of validation of the extracted features,an adaptive genetic random resonance early fault diagnosis method for rolling bearings was proposed.compared with the existing methods,the AGSR(Adaptive Genetic Stochastic Resonance)method uses genetic algorithms to optimize the system parameters,and further optimizes the parameters while considering the interaction between the parameters.This method can effectively extract the weak fault features of the bearing.In order to verify the effect of feature extraction,the feature signal extracted by AGSR method was input into the Fully connected neural network for fault diagnosis.the practicality of the algorithm is verified by simulation data and rolling bearing experimental data.the results show that the proposed method can effectively detect the early weak features of rolling bearings,and the fault diagnosis effect is better than the existing methods.
基金supports from National Science Foundation of China(Grant No.51777121).
文摘Aiming at the fact that the rotor winding inter-turn weak faults can hardly be detected due to the strong electromagnetic coupling effect in the excitation system,an interval observer based on current residual is designed.Firstly,the mechanism of the inter-turn short circuit of the rotor winding in the excitation system is modeled under the premise of stable working conditions,and electromagnetic decoupling and system simplification are carried out through Park Transform.An interval observer is designed based on the current residual in the two-phase coordinate system,and the sensitive and stable conditions of the observer is preset.The fault diagnosis process based on the interval observer is formulated,and the observer gain matrix is convexly optimized by linear matrix inequality.The numerical simulation and experimental results show that the inter-turn short circuit weak fault is hardly detected directly through the current signal,but the fault is quickly and accurately diagnosed through the residual internal observer.Compared with the traditional fault diagnosis method based on excitation current,the diagnosis speed and accuracy are greatly improved,and the probability of misdiagnosis also decreases.This method provides a theoretical basis for weak fault identification of excitation systems,and is of great significance for the operation and maintenance of excitation systems.
