This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.展开更多
In this paper we propose an equation model of system-level fault diagnoses, and construct corresponding theory and algorithms. People can turn any PMC model on ex-test into an equivalent equation (or a system of equat...In this paper we propose an equation model of system-level fault diagnoses, and construct corresponding theory and algorithms. People can turn any PMC model on ex-test into an equivalent equation (or a system of equations), and find all consistent fault patterns based on the equation model. We can also find all fault patterns, in which the fault node numbers are less than or equal to t without supposing t-diagnosable. It is not impossible for all graphic models.展开更多
A state/event fault tree(SEFT)is a modeling technique for describing the causal chains of events leading to failure in software-controlled complex systems.Such systems are ubiquitous in all areas of everyday life,and ...A state/event fault tree(SEFT)is a modeling technique for describing the causal chains of events leading to failure in software-controlled complex systems.Such systems are ubiquitous in all areas of everyday life,and safety and reliability analyses are increasingly required for these systems.SEFTs combine elements from the traditional fault tree with elements from state-based techniques.In the context of the real-time safety-critical systems,SEFTs do not describe the time properties and important timedependent system behaviors that can lead to system failures.Further,SEFTs lack the precise semantics required for formally modeling time behaviors.In this paper,we present a qualitative analysis method for SEFTs based on transformation from SEFT to timed automata(TA),and use the model checker UPPAAL to verify system requirements’properties.The combination of SEFT and TA is an important step towards an integrated design and verification process for real-time safety-critical systems.Finally,we present a case study of a powerboat autopilot system to confirm our method is viable and valid after achieving the verification goal step by step.展开更多
Computational complexity of complex system multiple fault diagnosis is a puzzle at all times. Based on the well known Mozetic＇s approach, a novel hierarchical model-based diagnosis methodology is put forward for impr...Computational complexity of complex system multiple fault diagnosis is a puzzle at all times. Based on the well known Mozetic＇s approach, a novel hierarchical model-based diagnosis methodology is put forward for improving efficency of multi-fault recognition and localization. Structural abstraction and weighted fault propagation graphs are combined to build diagnosis model. The graphs have weighted arcs with fault propagation probabilities and propagation strength. For solving the problem of coupled faults, two diagnosis strategies are used： one is the Lagrangian relaxation and the primal heuristic algorithms; another is the method of propagation strength. Finally, an applied example shows the applicability of the approach and experimental results are given to show the superiority of the presented technique.展开更多
A fault injection model-oriented testing strategy was proposed for detecting component vulnerabilities.A fault injection model was defined,and the faults were injected into the tested component based on the fault inje...A fault injection model-oriented testing strategy was proposed for detecting component vulnerabilities.A fault injection model was defined,and the faults were injected into the tested component based on the fault injection model to trigger security exceptions.The testing process could be recorded by the monitoring mechanism of the strategy,and the monitoring information was written into the security log.The component vulnerabilities could be detected by the detecting algorithm through analyzing the security log.Lastly,some experiments were done in an integration testing platform to verify the applicability of the strategy.The experimental results show that the strategy is effective and operable.The detecting rate is more than 90%for vulnerability components.展开更多
开发有效的风机电磁暂态模型是进行海上风电并网研究的基础。根据不同生产厂家所提供的直驱风机模型,提出了一种基于厂家黑盒模型的直驱风机电磁暂态建模方法。利用厂家所提供的模型测试数据解析风机的故障响应特性,推导了不同厂家在电...开发有效的风机电磁暂态模型是进行海上风电并网研究的基础。根据不同生产厂家所提供的直驱风机模型,提出了一种基于厂家黑盒模型的直驱风机电磁暂态建模方法。利用厂家所提供的模型测试数据解析风机的故障响应特性,推导了不同厂家在电压故障下有功和无功功率响应表达式,包括故障期间的穿越控制过程及不同过程间的暂态切换策略。提出了加权平均压降的风电场等值方法,采用自主研发的全电磁暂态仿真软件(power system model,PSModel)对我国广东某海上风电场进行全电磁暂态建模。根据稳态潮流及暂态特性结果验证了模型可通过系统测试,且有效实现了海上风电场的全电磁暂态建模,从而为该风场接入大电网后的安全稳定分析提供了研究基础。展开更多
为解决风电机组故障诊断中故障样本不足而导致模型准确率不高的问题,将当下备受关注的数据增强方法-去噪扩散概率模型(denoising diffusion probability model,DDPM)引入到故障诊断领域以生成大量高质量的故障样本数据集。因此,结合Tran...为解决风电机组故障诊断中故障样本不足而导致模型准确率不高的问题,将当下备受关注的数据增强方法-去噪扩散概率模型(denoising diffusion probability model,DDPM)引入到故障诊断领域以生成大量高质量的故障样本数据集。因此,结合Transformer网络,提出了一种DDPM-Transformer风电机组故障样本生成方法。首先,将用于计算机视觉图像生成领域的DDPM模型应用于风电机组故障诊断领域中,通过前向加噪过程将数据逐渐转化为噪声,再通过逆向去噪过程将噪声逐步恢复为原始数据,实现从噪声中生成故障数据,解决数据不平衡问题;其次,通过对原始DDPM中使用的U-net模块进行改进,使用Transformer模型替换U-net网络,利用扩散后的数据和添加的噪声训练Transformer模型,实现噪声预测,以提高故障数据的生成质量;最后,使用多种生成模型评价指标对生成的故障数据进行评价,在监督控制和数据采集系统(supervisory control and data acquisition,SCADA)故障数据生成中论证改进DDPM-Transformer模型的性能。通过试验证明,所提DDPM-Transformer模型与现有的生成模型相比,最大均值异(maximum mean discrepancy,MMD)最大提升0.13,峰值信噪比(peak signal to noise ratio,PSNR)最大提升7.8。所提模型可以有效地生成质量更高的风电机组故障样本,从而基于该样本集辅助训练基于深度学习的故障诊断模型,可以使诊断模型具有更高精度和良好的稳定性。展开更多
基金supported by the National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
基金Project supported by the National Natural Science Foundation of China! (No.69973016).
文摘In this paper we propose an equation model of system-level fault diagnoses, and construct corresponding theory and algorithms. People can turn any PMC model on ex-test into an equivalent equation (or a system of equations), and find all consistent fault patterns based on the equation model. We can also find all fault patterns, in which the fault node numbers are less than or equal to t without supposing t-diagnosable. It is not impossible for all graphic models.
基金supported by the National Natural Science Foundation of China(11832012)
文摘A state/event fault tree(SEFT)is a modeling technique for describing the causal chains of events leading to failure in software-controlled complex systems.Such systems are ubiquitous in all areas of everyday life,and safety and reliability analyses are increasingly required for these systems.SEFTs combine elements from the traditional fault tree with elements from state-based techniques.In the context of the real-time safety-critical systems,SEFTs do not describe the time properties and important timedependent system behaviors that can lead to system failures.Further,SEFTs lack the precise semantics required for formally modeling time behaviors.In this paper,we present a qualitative analysis method for SEFTs based on transformation from SEFT to timed automata(TA),and use the model checker UPPAAL to verify system requirements’properties.The combination of SEFT and TA is an important step towards an integrated design and verification process for real-time safety-critical systems.Finally,we present a case study of a powerboat autopilot system to confirm our method is viable and valid after achieving the verification goal step by step.
文摘Computational complexity of complex system multiple fault diagnosis is a puzzle at all times. Based on the well known Mozetic＇s approach, a novel hierarchical model-based diagnosis methodology is put forward for improving efficency of multi-fault recognition and localization. Structural abstraction and weighted fault propagation graphs are combined to build diagnosis model. The graphs have weighted arcs with fault propagation probabilities and propagation strength. For solving the problem of coupled faults, two diagnosis strategies are used： one is the Lagrangian relaxation and the primal heuristic algorithms; another is the method of propagation strength. Finally, an applied example shows the applicability of the approach and experimental results are given to show the superiority of the presented technique.
基金Project(513150601)supported by the National Pre-Research Project Foundation of China
文摘A fault injection model-oriented testing strategy was proposed for detecting component vulnerabilities.A fault injection model was defined,and the faults were injected into the tested component based on the fault injection model to trigger security exceptions.The testing process could be recorded by the monitoring mechanism of the strategy,and the monitoring information was written into the security log.The component vulnerabilities could be detected by the detecting algorithm through analyzing the security log.Lastly,some experiments were done in an integration testing platform to verify the applicability of the strategy.The experimental results show that the strategy is effective and operable.The detecting rate is more than 90%for vulnerability components.
文摘开发有效的风机电磁暂态模型是进行海上风电并网研究的基础。根据不同生产厂家所提供的直驱风机模型,提出了一种基于厂家黑盒模型的直驱风机电磁暂态建模方法。利用厂家所提供的模型测试数据解析风机的故障响应特性,推导了不同厂家在电压故障下有功和无功功率响应表达式,包括故障期间的穿越控制过程及不同过程间的暂态切换策略。提出了加权平均压降的风电场等值方法,采用自主研发的全电磁暂态仿真软件(power system model,PSModel)对我国广东某海上风电场进行全电磁暂态建模。根据稳态潮流及暂态特性结果验证了模型可通过系统测试,且有效实现了海上风电场的全电磁暂态建模,从而为该风场接入大电网后的安全稳定分析提供了研究基础。
文摘为解决风电机组故障诊断中故障样本不足而导致模型准确率不高的问题,将当下备受关注的数据增强方法-去噪扩散概率模型(denoising diffusion probability model,DDPM)引入到故障诊断领域以生成大量高质量的故障样本数据集。因此,结合Transformer网络,提出了一种DDPM-Transformer风电机组故障样本生成方法。首先,将用于计算机视觉图像生成领域的DDPM模型应用于风电机组故障诊断领域中,通过前向加噪过程将数据逐渐转化为噪声,再通过逆向去噪过程将噪声逐步恢复为原始数据,实现从噪声中生成故障数据,解决数据不平衡问题;其次,通过对原始DDPM中使用的U-net模块进行改进,使用Transformer模型替换U-net网络,利用扩散后的数据和添加的噪声训练Transformer模型,实现噪声预测,以提高故障数据的生成质量;最后,使用多种生成模型评价指标对生成的故障数据进行评价,在监督控制和数据采集系统(supervisory control and data acquisition,SCADA)故障数据生成中论证改进DDPM-Transformer模型的性能。通过试验证明,所提DDPM-Transformer模型与现有的生成模型相比,最大均值异(maximum mean discrepancy,MMD)最大提升0.13,峰值信噪比(peak signal to noise ratio,PSNR)最大提升7.8。所提模型可以有效地生成质量更高的风电机组故障样本,从而基于该样本集辅助训练基于深度学习的故障诊断模型,可以使诊断模型具有更高精度和良好的稳定性。
