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Fault detection and health monitoring of high-power thyristor converter based on long short-term memory in nuclear fusion
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作者 Ling ZHANG Ge GAO Li JIANG 《Plasma Science and Technology》 2025年第4期64-73,共10页
This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-t... This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor. 展开更多
关键词 fault detection and health monitoring high-power supply thyristor converter long short-term memory(LSTM) nuclear fusion(Some figures may appear in colour only in the online journal)
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Generalized autoencoder-based fault detection method for traction systems with performance degradation
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作者 Chao Cheng Wenyu Liu +1 位作者 Lu Di Shenquan Wang 《High-Speed Railway》 2024年第3期180-186,共7页
Fault diagnosis of traction systems is important for the safety operation of high-speed trains.Long-term operation of the trains will degrade the performance of systems,which decreases the fault detection accuracy.To ... Fault diagnosis of traction systems is important for the safety operation of high-speed trains.Long-term operation of the trains will degrade the performance of systems,which decreases the fault detection accuracy.To solve this problem,this paper proposes a fault detection method developed by a Generalized Autoencoder(GAE)for systems with performance degradation.The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation.Regardless of the probability distribution,it can handle any data,and the GAE has extremely high sensitivity in anomaly detection.Finally,the effectiveness of this method is verified through the Traction Drive Control System(TDCS)platform.At different performance degradation levels,our method’s experimental results are superior to traditional methods. 展开更多
关键词 Performance degradation Generalized autoencoder fault detection Traction control systems High-speed trains
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Incipient mechanical fault detection based on multifractal and MTS methods 被引量:8
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作者 Hu Jinqiu Zhang Laibin Liang Wei Wang Zhaohui 《Petroleum Science》 SCIE CAS CSCD 2009年第2期208-216,共9页
An incipient mechanical fault detection method, combining multifractal theory and Mahalanobis-Taguchi system (MTS), which is based on statistical technology, is proposed in this paper. Multifractal features of vibra... An incipient mechanical fault detection method, combining multifractal theory and Mahalanobis-Taguchi system (MTS), which is based on statistical technology, is proposed in this paper. Multifractal features of vibration signals obtained from machine state monitoring are extracted by multifractal spectrum analysis and generalized fractal dimensions. Considering the situation of mass samples of normal mechanical running state and few fault states, the feature parameters corresponding to different mechanical running states are further optimized by a statistical method, based on which incipient faults are subsequently identified and diagnosed accurately. Experimental results proved that the method combining multifractal theory and MTS can be used for incipient fault state recognition effectively during the mechanical running process, and the accuracy of fault state identification is improved. 展开更多
关键词 Incipient fault fault detection MULTIFRACTAL Mahalanobis-Taguchi system (MTS)
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Twin model-based fault detection and tolerance approach for in-core self-powered neutron detectors 被引量:4
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作者 Jing Chen Yan-Zhen Lu +2 位作者 Hao Jiang Wei-Qing Lin Yong Xu 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第8期86-99,共14页
The in-core self-powered neutron detector(SPND)acts as a key measuring device for the monitoring of parameters and evaluation of the operating conditions of nuclear reactors.Prompt detection and tolerance of faulty SP... The in-core self-powered neutron detector(SPND)acts as a key measuring device for the monitoring of parameters and evaluation of the operating conditions of nuclear reactors.Prompt detection and tolerance of faulty SPNDs are indispensable for reliable reactor management.To completely extract the correlated state information of SPNDs,we constructed a twin model based on a generalized regression neural network(GRNN)that represents the common relationships among overall signals.Faulty SPNDs were determined because of the functional concordance of the twin model and real monitoring sys-tems,which calculated the error probability distribution between the model outputs and real values.Fault detection follows a tolerance phase to reinforce the stability of the twin model in the case of massive failures.A weighted K-nearest neighbor model was employed to reasonably reconstruct the values of the faulty signals and guarantee data purity.The experimental evaluation of the proposed method showed promising results,with excellent output consistency and high detection accuracy for both single-and multiple-point faulty SPNDs.For unexpected excessive failures,the proposed tolerance approach can efficiently repair fault behaviors and enhance the prediction performance of the twin model. 展开更多
关键词 Self-powered neutron detector Twin model fault detection fault tolerance Generalized regression neural network Nuclear power plant
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Research on Low Energy Consumption Distributed Fault Detection Mechanism in Wireless Sensor Network 被引量:1
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作者 Shuang Jia Lin Ma Danyang Qin 《China Communications》 SCIE CSCD 2019年第3期179-189,共11页
Wireless sensor network is an important technical support for ubiquitous communication. For the serious impacts of network failure caused by the unbalanced energy consumption of sensor nodes, hardware failure and atta... Wireless sensor network is an important technical support for ubiquitous communication. For the serious impacts of network failure caused by the unbalanced energy consumption of sensor nodes, hardware failure and attacker intrusion on data transmission, a low energy consumption distributed fault detection mechanism in wireless sensor network(LEFD) is proposed in this paper. Firstly, the time correlation information of nodes is used to detect fault nodes in LEFD, and then the spatial correlation information is adopted to detect the remaining fault nodes, so as to check the states of nodes comprehensively and improve the efficiency of data transmission. In addition, the nodes do not need to exchange information with their neighbor nodes in the initial detection process since LEFD adopts the data sensed by node itself to detect some types of faults, thus reducing the energy consumption of nodes effectively. Finally, LEFD also considers the nodes that may have transient faults. Performance analysis and simulation results show that the proposed detection mechanism can improve the transmission performance and reduce the energy consumption of network effectively. 展开更多
关键词 wireless sensor network low energy CONSUMPTION fault detection detection ACCURACY
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A Method for Node Fault Detection in Wireless Sensor Networks 被引量:3
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作者 高志鹏 黄日茂 +1 位作者 陈颖慧 芮兰兰 《China Communications》 SCIE CSCD 2011年第1期28-34,共7页
To reduce excessive computing and communication loads of traditional fault detection methods,a neighbor-data analysis based node fault detection method is proposed.First,historical data is analyzed to confirm the conf... To reduce excessive computing and communication loads of traditional fault detection methods,a neighbor-data analysis based node fault detection method is proposed.First,historical data is analyzed to confirm the confidence level of sensor nodes.Then a node's reading data is compared with neighbor nodes' which are of good confidence level.Decision can be made whether this node is a failure or not.Simulation shows this method has good effect on fault detection accuracy and transient fault tolerance,and never transfers communication and computing overloading to sensor nodes. 展开更多
关键词 wireless sensor network fault detection neighbor nodes
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Mine-hoist fault-condition detection based on the wavelet packet transform and kernel PCA 被引量:3
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作者 XIA Shi-xiong NIU Qiang ZHOU Yong ZHANG Lei 《Journal of China University of Mining and Technology》 EI 2008年第4期567-570,共4页
A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Princ... A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Principal Compo- nent Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet packet transform is a novel technique of signal processing that possesses excellent characteristics of time-frequency localization. It is suitable for analysing time-varying or transient signals. KPCA maps the original input features into a higher dimension feature space through a non-linear mapping. The principal components are then found in the higher dimen- sion feature space. The KPCA transformation was applied to extracting the main nonlinear features from experimental fault feature data after wavelet packet transformation. The results show that the proposed method affords credible fault detection and identification. 展开更多
关键词 kernel method PCA KPCA fault condition detection
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Fault Detection Based on Incremental Locally Linear Embedding for Satellite TX-I 被引量:1
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作者 程月华 胡国飞 +2 位作者 陆宁云 姜斌 邢琰 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2015年第6期600-609,共10页
A fault detection method based on incremental locally linear embedding(LLE)is presented to improve fault detecting accuracy for satellites with telemetry data.Since conventional LLE algorithm cannot handle incremental... A fault detection method based on incremental locally linear embedding(LLE)is presented to improve fault detecting accuracy for satellites with telemetry data.Since conventional LLE algorithm cannot handle incremental learning,an incremental LLE method is proposed to acquire low-dimensional feature embedded in high-dimensional space.Then,telemetry data of Satellite TX-I are analyzed.Therefore,fault detection are performed by analyzing feature information extracted from the telemetry data with the statistical indexes T2 and squared prediction error(SPE)and SPE.Simulation results verify the fault detection scheme. 展开更多
关键词 incremental locally linear embedding(LLE) telemetry data fault detection dimensionality reduction statistical indexes
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Experimental study on wheeled vehicle hydro-pneumatic suspension fault detection
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作者 赵丰 管继富 +1 位作者 顾亮 雷雪媛 《Journal of Beijing Institute of Technology》 EI CAS 2016年第2期181-186,共6页
A four-channel MTS road simulation system,which is used to regenerate the acceleration signal at the axle head is presented. A new fault detection method is proposed,which is based on the remote parameter control( RP... A four-channel MTS road simulation system,which is used to regenerate the acceleration signal at the axle head is presented. A new fault detection method is proposed,which is based on the remote parameter control( RPC) technology for vehicle hydro-pneumatic suspension system. The transfer function between the drive signals and the axle head acceleration should be identified before the RPC iterative calculation on a computer. By contrasting with the desired frequency response functions( FRF),excited through the sample spectrum of road,the iterative convergence speed of the drive function and weighted error are used to detect faults existing in the vehicle's suspension. Experimental results show that during the process of regeneration of the acceleration signal at the axle head,the characteristics of failure of the hydro-pneumatic spring are changed randomly resulting in a dramatic increase in calculation of the RPC iterative,which enables relatively large iterative convergence errors. This method can quickly detect and locate a suspension fault and is a simple bench test way in suspension fault detection. 展开更多
关键词 hydro-pneumatic suspension fault detection iterative calculation
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INTELLIGENT DECISION ALGORITHM FOR FAULT DETECTION AND ITS APPLICATION
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作者 Wu Jianjun Zhang Yulin Chen Qizhi(Department of Aerospace Technology, NUDT,Changsha, 410073 ) 《国防科技大学学报》 EI CAS CSCD 北大核心 1995年第3期33-40,共8页
INTELLIGENTDECISIONALGORITHMFORFAULTDETECTIONANDITSAPPLICATIONWuJianjun;ZhangYulin;ChenQizhi(DepartmentofAer... INTELLIGENTDECISIONALGORITHMFORFAULTDETECTIONANDITSAPPLICATIONWuJianjun;ZhangYulin;ChenQizhi(DepartmentofAerospaceTechnology,... 展开更多
关键词 故障检测 智能决策 算法
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Design of Heading Fault-Tolerant System for Underwater Vehicles Based on Double-Criterion Fault Detection Method
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作者 Yanhui Wei Jing Liu +1 位作者 Shenggong Hao Jiaxing Hu 《Journal of Marine Science and Application》 CSCD 2019年第4期530-541,共12页
This paper proposes a heading fault tolerance scheme for operation-level underwater robots subject to external interference.The scheme is based on a double-criterion fault detection method using a redundant structure ... This paper proposes a heading fault tolerance scheme for operation-level underwater robots subject to external interference.The scheme is based on a double-criterion fault detection method using a redundant structure of a dual electronic compass.First,two subexpansion Kalman filters are set up to fuse data with an inertial attitude measurement system.Then,fault detection can effectively identify the fault sensor and fault source.Finally,a fault-tolerant algorithm is used to isolate and alarm the faulty sensor.The program can effectively detect the constant magnetic field interference,change the magnetic field interference and small transient magnetic field interference,and conduct fault tolerance control in time to ensure the heading accuracy of the system.Test verification shows that the system is practical and effective. 展开更多
关键词 Underwaterrobot Headingfault tolerance Redundant structure Double-criteria failuredetection FederatedKalman filter Electronic compass
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基于AHRFaultSegNet深度学习网络的地震数据断层自动识别 被引量:1
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作者 李克文 李文韬 +2 位作者 窦一民 朱信源 阳致煊 《石油地球物理勘探》 EI CSCD 北大核心 2024年第6期1225-1234,共10页
断层识别是地震数据解释的重要环节之一。深度学习技术的发展有效提高了断层自动识别的效率和准确性。然而,目前在断层的自动识别任务中,如何准确捕捉断层细微结构并有效抵抗噪声干扰仍然是一个具有挑战性的问题。为此,在HRNet网络的基... 断层识别是地震数据解释的重要环节之一。深度学习技术的发展有效提高了断层自动识别的效率和准确性。然而,目前在断层的自动识别任务中,如何准确捕捉断层细微结构并有效抵抗噪声干扰仍然是一个具有挑战性的问题。为此,在HRNet网络的基础上,构建了一种基于解耦自注意力机制的高分辨率断层识别网络模型AHRFaultSegNet。对于自注意力机制解耦,结合空间注意力和通道注意力,代替HRNet中并行传播的卷积层,在减少传统自注意力机制计算量的同时,模型可以在全局范围内计算输入特征的相关性,更准确地建模非局部特征;对解耦自注意力使用残差连接来保留原始特征,在加速模型训练的同时,使模型能够更好地保持细节信息。实验结果表明,所提出的网络模型在Dice、Fmeasure、IoU、Precision、Recall等性能评价指标上均优于其他常见的断层自动识别网络模型。通过对合成地震数据与实际地震数据等进行测试,证明了该方法对断层细微结构具有良好的识别效果并且具有良好的抗噪能力。 展开更多
关键词 断层检测识别 深度学习 解耦自注意力机制 残差连接
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Analysis of weak signal detection based on tri-stable system under Levy noise 被引量:3
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作者 贺利芳 崔莹莹 +2 位作者 张天骐 张刚 宋莹 《Chinese Physics B》 SCIE EI CAS CSCD 2016年第6期85-94,共10页
Stochastic resonance system is an effective method to extract weak signal.However,system output is directly influenced by system parameters.Aiming at this,the Levy noise is combined with a tri-stable stochastic resona... Stochastic resonance system is an effective method to extract weak signal.However,system output is directly influenced by system parameters.Aiming at this,the Levy noise is combined with a tri-stable stochastic resonance system.The average signal-to-noise ratio gain is regarded as an index to measure the stochastic resonance phenomenon.The characteristics of tri-stable stochastic resonance under Levy noise is analyzed in depth.First,the method of generating Levy noise,the effect of tri-stable system parameters on the potential function and corresponding potential force are presented in detail.Then,the effects of tri-stable system parameters w,a,b,and Levy noise intensity amplification factor D on the resonant output can be explored with different Levy noises.Finally,the tri-stable stochastic resonance system is applied to the bearing fault detection.Simulation results show that the stochastic resonance phenomenon can be induced by tuning the system parameters w,a,and b under different distributions of Levy noise,then the weak signal can be detected.The parameter intervals which can induce stochastic resonances are approximately equal.Moreover,by adjusting the intensity amplification factor D of Levy noise,the stochastic resonances can happen similarly.In bearing fault detection,the detection effect of the tri-stable stochastic resonance system is superior to the bistable stochastic resonance system. 展开更多
关键词 tri-stable system Levy noise stochastic resonance bearing fault detection
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Weak signal detection method based on novel composite multistable stochastic resonance 被引量:1
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作者 焦尚彬 高蕊 +1 位作者 薛琼婕 史佳强 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第5期178-187,共10页
The weak signal detection method based on stochastic resonance is usually used to extract and identify the weak characteristic signal submerged in strong noise by using the noise energy transfer mechanism.We propose a... The weak signal detection method based on stochastic resonance is usually used to extract and identify the weak characteristic signal submerged in strong noise by using the noise energy transfer mechanism.We propose a novel composite multistable stochastic-resonance(NCMSR)model combining the Gaussian potential model and an improved bistable model.Compared with the traditional multistable stochastic resonance method,all the parameters in the novel model have no symmetry,the output signal-to-noise ratio can be optimized and the output amplitude can be improved by adjusting the system parameters.The model retains the advantages of continuity and constraint of the Gaussian potential model and the advantages of the improved bistable model without output saturation,the NCMSR model has a higher utilization of noise.Taking the output signal-to-noise ratio as the index,weak periodic signal is detected based on the NCMSR model in Gaussian noise andαnoise environment respectively,and the detection effect is good.The application of NCMSR to the actual detection of bearing fault signals can realize the fault detection of bearing inner race and outer race.The outstanding advantages of this method in weak signal detection are verified,which provides a theoretical basis for industrial practical applications. 展开更多
关键词 weak signal detection composite multistable stochastic resonance bearing fault detection
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基于GGD-EfficientNet和声纹识别的风力发电机齿轮箱故障诊断
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作者 廖力达 陈伟克 +3 位作者 罗晓 舒王咏 张芝铭 代军 《太阳能学报》 北大核心 2025年第4期570-578,共9页
针对风力发电机齿轮箱齿轮故障时的噪声提出一种基于分组全局上下文网络(GE-GCNet)与深度可分离卷积(DSCConv)结合的效率神经网络(GGD-EfficientNet)和声纹识别的齿轮箱故障诊断方法。首先通过实验获取齿轮箱故障齿轮的噪声信号,并根据... 针对风力发电机齿轮箱齿轮故障时的噪声提出一种基于分组全局上下文网络(GE-GCNet)与深度可分离卷积(DSCConv)结合的效率神经网络(GGD-EfficientNet)和声纹识别的齿轮箱故障诊断方法。首先通过实验获取齿轮箱故障齿轮的噪声信号,并根据齿轮状态分为6类。然后,使用Log-Mel谱的方法提取噪声信号语谱图。考虑到效率卷积神经网络(EfficientNet)对齿轮故障语谱图特征提取能力不足等缺点,在EfficientNet的基础上,结合分组卷积改进的GE-GCNet和DSCConv,提出一种高性能的齿轮故障诊断模型GGD-EfficientNet。实验表明:所提方法能在齿轮箱故障齿轮语谱图数据集下准确率达到99.7%。所提模型能从数据集中对故障类型进行有效分类,可有效帮助诊断齿轮箱中齿轮故障。 展开更多
关键词 风力发电机 齿轮 故障检测 GGD-EfficientNet 声纹识别
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基于图注意力和Transformer的神经网络故障检测方法
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作者 程超 李凌枫 《长春工业大学学报》 2025年第1期1-9,F0003,共10页
针对永磁同步电机长时间工作情况下存在的隐性故障检测效果不佳的问题,提出一种将图注意力神经网络(GAT)和Transformer结合的故障检测方法。该方法结合了Transformer与GAT各自的优点,通过编码器(Encoder)和图注意力层可以有效利用数据... 针对永磁同步电机长时间工作情况下存在的隐性故障检测效果不佳的问题,提出一种将图注意力神经网络(GAT)和Transformer结合的故障检测方法。该方法结合了Transformer与GAT各自的优点,通过编码器(Encoder)和图注意力层可以有效利用数据的时序信息和空域结构关联信息,提高诊断的准确精度和模型的泛化性能,以实现故障检测。所提方法在永磁同步电动机实验平台上自建的故障数据集进行实验验证,该方法可达到91.85%准确率,优于传统机器学习和其他深度学习方法。 展开更多
关键词 计算机应用 故障检测 深度学习 图神经网络 注意力机制 永磁同步电机
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基于数字孪生和机器学习的卫星未知故障检测方法
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作者 沈英龙 蔡君亮 +1 位作者 林佳伟 杨帆 《中国空间科学技术(中英文)》 北大核心 2025年第1期46-58,共13页
卫星传统故障诊断方法及现有的数据驱动诊断方法都存在无法找出异于已知故障类型的未知故障的问题,可靠性与安全性较低。针对上述问题,提出基于卫星数字孪生体和多种机器学习模型的故障诊断与未知故障检测方法。首先,通过卫星数字孪生... 卫星传统故障诊断方法及现有的数据驱动诊断方法都存在无法找出异于已知故障类型的未知故障的问题,可靠性与安全性较低。针对上述问题,提出基于卫星数字孪生体和多种机器学习模型的故障诊断与未知故障检测方法。首先,通过卫星数字孪生产生覆盖各种类型故障的仿真数据,并利用XGBoost分类模型和卫星真实故障样本验证了数字孪生数据的高仿真性,实现了已知故障类型的诊断。在此基础上,考虑到现有诊断方法无法精准识别未知类型故障的发生,提出一种分布外检测模型Con-DAGMM,通过正常数据和已知类型故障数据训练模型,实现了对未知故障的及时预警。利用数字孪生数据与在轨卫星真实故障数据进行实验,实验结果表明,所提方法故障诊断精度高,在测试数据上的平均准确率达到98.8%,且Con-DAGMM实现了高性能的未知故障检测,在精准率、召回率和F_(1)分数上优于Deep-SVDD等对比方法。结果表明,卫星数字孪生可以克服卫星历史数据中故障样本稀缺的问题,且分布外检测的思路能成功应用于卫星未知故障的预警,提高了在轨卫星的安全性与可靠性。 展开更多
关键词 卫星控制系统 未知故障检测 故障诊断 数字孪生 机器学习 分布外检测
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多模型的运载火箭姿态控制系统故障检测与隔离
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作者 谢昌霖 程玉强 +1 位作者 杨述明 宋立军 《国防科技大学学报》 北大核心 2025年第2期60-67,共8页
针对运载火箭姿态控制系统结构复杂、故障高发的问题,提出一种基于多模型的故障检测与隔离算法。建立运载火箭小偏差姿态动力学模型,设计系统的卡尔曼滤波器;结合专用观测器思想,利用多个不同结构的卡尔曼滤波器组生成对应残差,使得单... 针对运载火箭姿态控制系统结构复杂、故障高发的问题,提出一种基于多模型的故障检测与隔离算法。建立运载火箭小偏差姿态动力学模型,设计系统的卡尔曼滤波器;结合专用观测器思想,利用多个不同结构的卡尔曼滤波器组生成对应残差,使得单个残差仅对于传感器或执行机构的某一故障敏感,并通过理论推导了故障隔离策略,以实现运载火箭不同故障类型的检测和隔离。仿真分析表明,无故障时,残差结果均没有超出设定阈值,算法未出现报警;传感器或执行机构故障时,提出的隔离策略可以准确定位故障,从而验证了该算法的有效性。 展开更多
关键词 运载火箭 故障检测与隔离 卡尔曼滤波器 传感器 执行机构
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基于声信号特征分析的水轮机故障检测
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作者 程俊棋 毕东杰 +4 位作者 彭礼彪 李西峰 谢永乐 王明义 李卓航 《水电能源科学》 北大核心 2025年第2期196-200,共5页
针对轴流转桨式水轮机的瞬态故障检测和故障分类问题,提出了一种基于声信号时域和频域特征分析的方法,即从水车室、风洞、蜗壳人孔门和尾水人孔门处采集的声信号中提取5种时域和7种频域特征序列,通过判断特征序列是否超过瞬态故障预警... 针对轴流转桨式水轮机的瞬态故障检测和故障分类问题,提出了一种基于声信号时域和频域特征分析的方法,即从水车室、风洞、蜗壳人孔门和尾水人孔门处采集的声信号中提取5种时域和7种频域特征序列,通过判断特征序列是否超过瞬态故障预警指数的方法进行瞬态故障监测,存在故障时对故障数据使用核支持向量机进行故障分类。利用该方法在铜街子水电站对6种典型故障进行测试,结果表明所提方法可以有效地检测轴流转桨式水轮机各类典型故障。 展开更多
关键词 轴流转桨式水轮机 故障检测 特征分析 时域特征 频域特征
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组合导航系统故障诊断和隔离算法研究综述
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作者 庾靖 李蓬蓬 +2 位作者 魏征 刘小汇 牟卫华 《无线电工程》 2025年第2期323-333,共11页
组合导航系统故障诊断和隔离算法对于增强系统可靠性和安全准确的导航至关重要。综述了近年来国内外组合导航系统故障诊断和隔离算法领域的研究进展。介绍了包括基于残差统计、模型架构以及数据驱动的故障检测算法和不同数据融合结构下... 组合导航系统故障诊断和隔离算法对于增强系统可靠性和安全准确的导航至关重要。综述了近年来国内外组合导航系统故障诊断和隔离算法领域的研究进展。介绍了包括基于残差统计、模型架构以及数据驱动的故障检测算法和不同数据融合结构下的故障隔离容错算法。分析了现有算法的性能和应用范围,并梳理了发展脉络以及面临的困难挑战。对算法的发展趋势进行了展望,为提高组合导航系统可靠性研究提供了思路。 展开更多
关键词 组合导航 故障检测 故障隔离 容错技术
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