基于燃气涡轮发动机由计划维修模式向视情维修(Condition Based Maintenance, CBM)模式转变、由被动保障向主动保障发展的实际需求,针对目前燃气涡轮发动机在故障预测与健康管理(Prognostics and Health Management, PHM)系统设计上存...基于燃气涡轮发动机由计划维修模式向视情维修(Condition Based Maintenance, CBM)模式转变、由被动保障向主动保障发展的实际需求,针对目前燃气涡轮发动机在故障预测与健康管理(Prognostics and Health Management, PHM)系统设计上存在的整体性与系统性不强的问题,通过开展燃气涡轮发动机的PHM技术研究,围绕故障诊断、寿命预测、保障决策等PHM系统的核心功能要素,提出了一种新型燃气涡轮发动机PHM系统架构。该架构包括实时监测模块与保障决策模块,实时监测模块主要基于测试性建模实现故障诊断逻辑设计以满足涡轮发动机故障检测与健康管理的实时性要求,保障决策模块为燃气涡轮发动机的CBM保障提供了一种数据驱动的决策生成方法,通过长短时记忆网络(Long Short-Term Memory, LSTM)算法预测关键部件寿命,以匹配最佳的维修保障方案,提升燃气涡轮发动机的保障效能,最后给出了该系统架构的部分核心功能的设计。展开更多
通过智慧化运维降本增效是提高中国城市轨道交通可持续发展能力的重要途径之一,将数字孪生技术应用在轨道交通车辆故障预测与健康管理(PHM)中,可以有效解决传统PHM的不足。本文提出了一种基于数字孪生技术的牵引变流器冷却系统PHM的构...通过智慧化运维降本增效是提高中国城市轨道交通可持续发展能力的重要途径之一,将数字孪生技术应用在轨道交通车辆故障预测与健康管理(PHM)中,可以有效解决传统PHM的不足。本文提出了一种基于数字孪生技术的牵引变流器冷却系统PHM的构建方法。首先构建了牵引变流冷却系统关键子系统物理实体的数字孪生体,包括机理模型和数字模型;然后通过虚实之间的数字映射和健康域的标定,并采用岭回归、循环神经网络、随机森林等方式进行模型训练,得到最优的模型组合;最后以变流器柜体温度预测进行了模型测试。结果表明,该模型的预测效果与实际系统的运行结果基本吻合,可以进一步作为变流器冷却系统故障预测和故障定位的开发基础。Reducing costs and increasing efficiency through Intelligent Operation and Maintenance (IOM) is one of the most important ways to improve the sustainable development of China’s urban rail transit. The application of digital twin technology in the Prognostics and Health Management (PHM) of rail transit vehicles can effectively address the shortcomings of traditional PHM methods. This paper proposes a construction method for a traction inverter cooling system PHM based on digital twin technology. Firstly, a digital twin of the physical entities of the key subsystems of the traction inverter cooling system was constructed, including the mechanism model and the digital model;then, the optimal model combination was obtained through the numerical mapping between the real and the imaginary and the calibration of the health domains, and the model training was carried out by using ridge regression, recurrent neural network, and random forests, etc.;finally, the model was tested for predicting the temperature of the inverter cabinet. The results showed that the prediction performance of the model was basically consistent with the operation results of the actual system, indicating that the model can be further used as a development foundation for the fault prediction and location of the inverter cooling system.展开更多
文摘基于燃气涡轮发动机由计划维修模式向视情维修(Condition Based Maintenance, CBM)模式转变、由被动保障向主动保障发展的实际需求,针对目前燃气涡轮发动机在故障预测与健康管理(Prognostics and Health Management, PHM)系统设计上存在的整体性与系统性不强的问题,通过开展燃气涡轮发动机的PHM技术研究,围绕故障诊断、寿命预测、保障决策等PHM系统的核心功能要素,提出了一种新型燃气涡轮发动机PHM系统架构。该架构包括实时监测模块与保障决策模块,实时监测模块主要基于测试性建模实现故障诊断逻辑设计以满足涡轮发动机故障检测与健康管理的实时性要求,保障决策模块为燃气涡轮发动机的CBM保障提供了一种数据驱动的决策生成方法,通过长短时记忆网络(Long Short-Term Memory, LSTM)算法预测关键部件寿命,以匹配最佳的维修保障方案,提升燃气涡轮发动机的保障效能,最后给出了该系统架构的部分核心功能的设计。
文摘通过智慧化运维降本增效是提高中国城市轨道交通可持续发展能力的重要途径之一,将数字孪生技术应用在轨道交通车辆故障预测与健康管理(PHM)中,可以有效解决传统PHM的不足。本文提出了一种基于数字孪生技术的牵引变流器冷却系统PHM的构建方法。首先构建了牵引变流冷却系统关键子系统物理实体的数字孪生体,包括机理模型和数字模型;然后通过虚实之间的数字映射和健康域的标定,并采用岭回归、循环神经网络、随机森林等方式进行模型训练,得到最优的模型组合;最后以变流器柜体温度预测进行了模型测试。结果表明,该模型的预测效果与实际系统的运行结果基本吻合,可以进一步作为变流器冷却系统故障预测和故障定位的开发基础。Reducing costs and increasing efficiency through Intelligent Operation and Maintenance (IOM) is one of the most important ways to improve the sustainable development of China’s urban rail transit. The application of digital twin technology in the Prognostics and Health Management (PHM) of rail transit vehicles can effectively address the shortcomings of traditional PHM methods. This paper proposes a construction method for a traction inverter cooling system PHM based on digital twin technology. Firstly, a digital twin of the physical entities of the key subsystems of the traction inverter cooling system was constructed, including the mechanism model and the digital model;then, the optimal model combination was obtained through the numerical mapping between the real and the imaginary and the calibration of the health domains, and the model training was carried out by using ridge regression, recurrent neural network, and random forests, etc.;finally, the model was tested for predicting the temperature of the inverter cabinet. The results showed that the prediction performance of the model was basically consistent with the operation results of the actual system, indicating that the model can be further used as a development foundation for the fault prediction and location of the inverter cooling system.
