The momentum wheel assumes a dominant role as an inertial actuator for satellite attitude control systems.Due to the effects of structural aging and external interference,the momentum wheel may experience the gradual ...The momentum wheel assumes a dominant role as an inertial actuator for satellite attitude control systems.Due to the effects of structural aging and external interference,the momentum wheel may experience the gradual emergence of irreversible faults.These fault features will become apparent in the telemetry signal transmitted by the momentum wheel.This paper introduces ADTWformer,a lightweight model for long-term prediction of time series,to analyze the time evolution trend and multi-dimensional data coupling mechanism of satellite momentum wheel faults.Moreover,the incorporation of the approximate Markov blanket with the maximum information coefficient presents a novel methodology for performing correlation analysis,providing significant perspectives from a data-centric standpoint.Ultimately,the creation of an adaptive alarm mechanism allows for the successful attainment of the momentum wheel fault warning by detecting the changes in the health status curves.The analysis methodology outlined in this article has exhibited positive results in identifying instances of satellite momentum wheel failure in two scenarios,thereby showcasing considerable promise for large-scale applications.展开更多
Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding ...Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding of a synchronous generator,a gate recurrent unit-convolutional neural network(GRU-CNN)model whose structural parameters were determined by improved particle swarm optimization(IPSO)is proposed.The outputs of the model are the excitation current and reactive power.The total offset distance,which is the fusion of the offset distance of the excitation current and offset distance of the reactive power,was selected as the fault judgment criterion.The fusion weights of the excitation current and reactive power were determined using the anti-entropy weighting method.The fault-warning threshold and fault-warning ratio were set according to the normal total offset distance,and the fault warning time was set according to the actual situation.The fault-warning time and fault-warning ratio were used to avoid misdiagnosis.The proposed method was verified experimentally.展开更多
The digital twin-driven performance model provides an attractive option for the warn gas-path faults of the gas turbines.However,three technical difficulties need to be solved:(1)low modeling precision caused by indiv...The digital twin-driven performance model provides an attractive option for the warn gas-path faults of the gas turbines.However,three technical difficulties need to be solved:(1)low modeling precision caused by individual differences between gas turbines,(2)poor solution efficiency due to excessive iterations,and(3)the false alarm and missing alarm brought by the traditional fixed threshold method.This paper proposes a digital twin model-based early warning method for gas-path faults that breaks through the above obstacles from three aspects.Firstly,a novel performance modeling strategy is proposed to make the simulation effect close to the actual gas turbine by fusing the mechanism model and measurement data.Secondly,the idea of controlling the relative accuracy of model parameters is developed.The introduction of an error module to the existing model can greatly shorten the modeling cycle.The third solution focuses on the early warning based on the digital twin model,which self-learns the alarm threshold of the warning feature of gas-path parameters using the kernel density estimation.The proposed method is utilized to analyze actual measured data of LM2500+,and the results verify that the new-built digital model has higher accuracy and better efficiency.The comparisons show that the proposed method shows evident superiority in early warning of performance faults for gas turbines over other methods.展开更多
Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of use...Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of useful information. A weak characteristic information extraction based on μ-SVD and local mean decomposition （LMD） is developed to address this problem. The basic principle of the method is as follows： Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and μ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.展开更多
Aiming at the problems of poor adaptability and insufficient fault prediction of traditional mechanical automation control systems in complex working conditions,a mechanical automation control system based on artifici...Aiming at the problems of poor adaptability and insufficient fault prediction of traditional mechanical automation control systems in complex working conditions,a mechanical automation control system based on artificial intelligence is designed.This design integrates expert control,fuzzy control,and neural network control technologies,and builds a hierarchical distributed architecture.Fault warning adopts threshold judgment and dynamic time warping pattern recognition technologies,and state monitoring realizes accurate analysis through multi-source data fusion and Kalman filtering algorithm.Practical applications show that this system can reduce the equipment failure rate by more than 30%.With the help of intelligent scheduling optimization,it can significantly improve production efficiency and reduce energy consumption,providing a reliable technical solution and practical path for the intelligent upgrade of the mechanical automation field.展开更多
Energy-storage technologies based on lithium-ion batteries are advancing rapidly.However,the occurrence of thermal runaway in batteries under extreme operating conditions poses serious safety concerns and potentially ...Energy-storage technologies based on lithium-ion batteries are advancing rapidly.However,the occurrence of thermal runaway in batteries under extreme operating conditions poses serious safety concerns and potentially leads to severe accidents.To address the detection and early warning of battery thermal runaway faults,this study conducted a comprehensive review of recent advances in lithium battery fault monitoring and early warning in energy-storage systems from various physical perspectives.The focus was electrical,thermal,acoustic,and mechanical aspects,which provide effective insights for energy-storage system safety enhancement.展开更多
基金supported by the Science Center Program of National Natural Science Foundation of China(62188101)the National Natural Science Foundation of China(61833009,61690212,51875119)+1 种基金the Heilongjiang Touyan Teamthe Guangdong Major Project of Basic and Applied Basic Research(2019B030302001)
文摘The momentum wheel assumes a dominant role as an inertial actuator for satellite attitude control systems.Due to the effects of structural aging and external interference,the momentum wheel may experience the gradual emergence of irreversible faults.These fault features will become apparent in the telemetry signal transmitted by the momentum wheel.This paper introduces ADTWformer,a lightweight model for long-term prediction of time series,to analyze the time evolution trend and multi-dimensional data coupling mechanism of satellite momentum wheel faults.Moreover,the incorporation of the approximate Markov blanket with the maximum information coefficient presents a novel methodology for performing correlation analysis,providing significant perspectives from a data-centric standpoint.Ultimately,the creation of an adaptive alarm mechanism allows for the successful attainment of the momentum wheel fault warning by detecting the changes in the health status curves.The analysis methodology outlined in this article has exhibited positive results in identifying instances of satellite momentum wheel failure in two scenarios,thereby showcasing considerable promise for large-scale applications.
文摘Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding of a synchronous generator,a gate recurrent unit-convolutional neural network(GRU-CNN)model whose structural parameters were determined by improved particle swarm optimization(IPSO)is proposed.The outputs of the model are the excitation current and reactive power.The total offset distance,which is the fusion of the offset distance of the excitation current and offset distance of the reactive power,was selected as the fault judgment criterion.The fusion weights of the excitation current and reactive power were determined using the anti-entropy weighting method.The fault-warning threshold and fault-warning ratio were set according to the normal total offset distance,and the fault warning time was set according to the actual situation.The fault-warning time and fault-warning ratio were used to avoid misdiagnosis.The proposed method was verified experimentally.
基金co-supported by the National Postdoctoral Program for Innovative Talent(No.BX20180031)。
文摘The digital twin-driven performance model provides an attractive option for the warn gas-path faults of the gas turbines.However,three technical difficulties need to be solved:(1)low modeling precision caused by individual differences between gas turbines,(2)poor solution efficiency due to excessive iterations,and(3)the false alarm and missing alarm brought by the traditional fixed threshold method.This paper proposes a digital twin model-based early warning method for gas-path faults that breaks through the above obstacles from three aspects.Firstly,a novel performance modeling strategy is proposed to make the simulation effect close to the actual gas turbine by fusing the mechanism model and measurement data.Secondly,the idea of controlling the relative accuracy of model parameters is developed.The introduction of an error module to the existing model can greatly shorten the modeling cycle.The third solution focuses on the early warning based on the digital twin model,which self-learns the alarm threshold of the warning feature of gas-path parameters using the kernel density estimation.The proposed method is utilized to analyze actual measured data of LM2500+,and the results verify that the new-built digital model has higher accuracy and better efficiency.The comparisons show that the proposed method shows evident superiority in early warning of performance faults for gas turbines over other methods.
基金This research was sponsored by the National Natural Science Foundation of China （Grant Nos. 51275052 and 51105041）, and the Key Project Supported by Beijing Natural Science Foundation （Grant No. 3131002）.
文摘Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of useful information. A weak characteristic information extraction based on μ-SVD and local mean decomposition （LMD） is developed to address this problem. The basic principle of the method is as follows： Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and μ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.
文摘Aiming at the problems of poor adaptability and insufficient fault prediction of traditional mechanical automation control systems in complex working conditions,a mechanical automation control system based on artificial intelligence is designed.This design integrates expert control,fuzzy control,and neural network control technologies,and builds a hierarchical distributed architecture.Fault warning adopts threshold judgment and dynamic time warping pattern recognition technologies,and state monitoring realizes accurate analysis through multi-source data fusion and Kalman filtering algorithm.Practical applications show that this system can reduce the equipment failure rate by more than 30%.With the help of intelligent scheduling optimization,it can significantly improve production efficiency and reduce energy consumption,providing a reliable technical solution and practical path for the intelligent upgrade of the mechanical automation field.
文摘Energy-storage technologies based on lithium-ion batteries are advancing rapidly.However,the occurrence of thermal runaway in batteries under extreme operating conditions poses serious safety concerns and potentially leads to severe accidents.To address the detection and early warning of battery thermal runaway faults,this study conducted a comprehensive review of recent advances in lithium battery fault monitoring and early warning in energy-storage systems from various physical perspectives.The focus was electrical,thermal,acoustic,and mechanical aspects,which provide effective insights for energy-storage system safety enhancement.
