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Fault Warning of Satellite Momentum Wheels With a Lightweight Transformer Improved by FastDTW 认领 引用 被引量：1
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作者 Yiming Gao Shi Qiu +2 位作者 Ming Liu Lixian Zhang Xibin Cao 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第3期539-549,共11页
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
关键词 Approximate Markov blanket fault early warning maximal information coefficient satellite momentum wheel
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A fault warning for inter-turn short circuit of excitation winding of synchronous generator based on GRU-CNN 认领 引用 被引量：9
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作者 Junqing Li Jing Liu Yating Chen 《Global Energy Interconnection》 EI CSCD 2022年第2期236-248,共13页
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
关键词 Synchronous generator Inter-turn short circuit Excitation winding Fault warning GRU-CNN IPSO
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Digital twin model of gas turbine and its application in warning of performance fault 认领 引用 被引量：12
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作者 Minghui HU Ya HE +3 位作者 Xinzhi LIN Ziyuan LU Zhinong JIANG Bo MA 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2023年第3期449-470,共22页
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
关键词 Gas turbine Digital twin model Gas-path fault warning Deviation degree analysis Performance simulation
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Weak characteristic information extraction from early fault of wind turbine generator gearboxKeywords wind turbine generator gearbox, B-singular value decomposition, local mean decomposition, weak characteristic information extraction, early fault warning 认领 引用 被引量：2
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作者 Xiaoli XU Xiuli LIU 《Frontiers of Mechanical Engineering》 SCIE CSCD 2017年第3期357-366,共10页
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
关键词 wind turbine generator gearbox μ-singular value decomposition local mean decomposition weak characteristic information extraction early fault warning
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Integrated operational monitoring and fault early warning system for wheat combine harvesters based on CAN bus 认领 引用
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作者 Weipeng Zhang Hongze Guo +5 位作者 Bo Zhao Suchun Liu Liming Zhou Fengzhu Wang Zongbin Li Yangchun Liu 《International Journal of Agricultural and Biological Engineering》 SCIE 2026年第1期170-178,共9页
The core objective of this study is to address critical challenges in the operational monitoring and fault early warning of wheat combine harvesters.To this end,this study designed a field-oriented multi-parameter det... The core objective of this study is to address critical challenges in the operational monitoring and fault early warning of wheat combine harvesters.To this end,this study designed a field-oriented multi-parameter detection system for wheat combine harvesters,which utilizes the CAN bus and virtual instrumentation.Key challenges in this field include three aspects:first,manual inspection is inefficient and lacks automated detection methods,making it difficult to meet the real-time requirements of large-scale operations;second,fault early warning accuracy is low,as single-parameter evaluation is prone to false positives and false negatives;third,monitoring parameters function in isolation,leading to significant data inconsistencies that hinder the early detection of potential faults.To address these issues,this study focuses on three key tasks:establishing a multi-parameter collaborative monitoring framework,optimizing hardware and communication protocols,and developing data processing methods for fault detection and warning.Specifically,sensors for fuel consumption,Hall-effect rotational speed,and strain-gauge torque are deployed at critical components of the harvester.The system then efficiently transmits operational status data via the CAN bus to a processing module,enabling remote real-time monitoring of the harvester’s comprehensive operational conditions.For the designed fault warning algorithm,it dynamically adjusts warning thresholds by comparing characteristic parameters with historical data,thereby achieving accurate fault identification and timely warning responses.This study innovatively transmitted multi-source sensor data through the high-anti-interference CAN bus and developed a fault warning algorithm incorporating feature recognition and dynamic thresholds.In simulated experiments,the measurement errors of both instantaneous and cumulative fuel consumption were≤5%,while the system achieved a warning accuracy of 97.3%and a response time of≤180 ms.This represents a 15.3-percentage-point improvement in accuracy compared to traditional single-parameter warning systems.Overall,this study addresses the challenge of multi-parameter integrated monitoring for wheat combine harvesters and provides a scalable technical solution for hardware integration and comprehensive data analysis.It also offers a reference for the intelligent upgrading of Chinese harvesters,which is expected to accelerate the transformation of agricultural mechanization toward precision and informatization. 展开更多
关键词 combine harvester comprehensive monitoring fault early warning CAN bus visualization system
Research on Multi-dimensional Collaborative Perception and Intelligent Operation & Maintenance Optimization of Urban Rail Transit Pantograph-Catenary System 认领 引用
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作者 LI Liang 《外文科技期刊数据库（文摘版）工程技术》 2026年第1期111-115,共5页
The pantograph-catenary system is the core of power transmission for urban rail transit trains, and its operating status determines the safety of train power supply and line operation. Aiming at the problems of tradit... The pantograph-catenary system is the core of power transmission for urban rail transit trains, and its operating status determines the safety of train power supply and line operation. Aiming at the problems of traditional pantograph-catenary operation and maintenance relying on manual work, delayed response and single monitoring dimension, this paper takes the project of Nanchang Metro Line 4 as the carrier to explore the integration path of trackside fixed and vehicle-mounted mobile monitoring technologies, and construct a multi-dimensional collaborative perception and monitoring system. It analyzes the architecture and characteristics of the two types of monitoring equipment, carries out accuracy verification, efficiency analysis and value evaluation combined with the measured data from October 2024 to August 2025, reveals the technical bottlenecks and puts forward optimization schemes. The research shows that the system can realize minute-level detection and meter-level positioning of pantograph-catenary faults, with a fault identification accuracy of over 95%, optimize the carbon skateboard replacement cycle by 15%, and reduce operation and maintenance costs by 10%. It provides support and paradigm for preventive operation and maintenance, and improves the intelligence and safety guarantee capability of pantograph-catenary operation and maintenance 展开更多
关键词 urban rail transit pantograph-catenary system collaborative perception intelligent operation and maintenance data fusion fault early warning
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Design of Mechanical Automation Control System Based on Artificial Intelligence 认领 引用
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作者 Zhen Sun 《Journal of Electronic Research and Application》 2025年第3期270-277,共8页
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
关键词 Artificial intelligence Mechanical automation Control system design Fault warning Intelligent monitoring
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Li-ion Battery Failure Warning Methods for Energy-storage Systems 认领 引用 被引量：20
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作者 Peihao Wu Nawei Lyu +2 位作者 Yuhang Song Xin Jiang Yang Jin 《Chinese Journal of Electrical Engineering》 EI CSCD 2024年第1期86-100,共15页
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
关键词 Lithium-ion batteries energy-storage systems thermal runaway characteristic signals fault warning
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