Building energy consumption accounts for nearly 40% of global energy consumption, HVAC （Heating, Ventilating, and Air Conditioning） systems are the major building energy consumers, and as one type of HVAC systems, t...Building energy consumption accounts for nearly 40% of global energy consumption, HVAC （Heating, Ventilating, and Air Conditioning） systems are the major building energy consumers, and as one type of HVAC systems, the heat pump air conditioning system, which is more energy-efficient compared to the traditional air conditioning system, is being more widely used to save energy. However, in northern China, extreme climatic conditions increase the cooling and heating load of the heat pump air conditioning system and accelerate the aging of the equipment, and the sensor may detect drifted parameters owing to climate change. This non-linear drifted parameter increases the false alarm rate of the fault detection and the need for unnecessary troubleshooting. In order to overcome the impact of the device aging and the drifted parameter, a Kalman filter and SPC （statistical process control） fault detection method are introduced in this paper. In this method, the model parameter and its standard variance can he estimated by Kalman filter based on the gray model and the real-time data of the air conditioning system. Further, by using SPC to construct the dynamic control limits, false alarm rate is reduced. And this paper mainly focuses on the cold machine failure in the component failure and its soft fault detection. This approach has been tested on a simulation model of the ＂Sino-German Energy Conservation Demonstration Center＂ building heat pump air-conditioning system in Shenyang, China, and the results show that the Kalman filter and SPC fault detection method is simple and highly efficient with a low false alarm rate, and it can deal with the difficulties caused by the extreme environment and the non-linear influence of the parameters, and what＇s more, it provides a good foundation for dynamic fault diagnosis and fault prediction analysis.展开更多
A flexible polarization demultiplexing method based on an adaptive Kalman filter（AKF） is proposed in which the process noise covariance has been estimated adaptively. The proposed method may significantly improve th...A flexible polarization demultiplexing method based on an adaptive Kalman filter（AKF） is proposed in which the process noise covariance has been estimated adaptively. The proposed method may significantly improve the adaptive capability of an extended Kalman filter（EKF） by adaptively estimating the unknown process noise covariance. Compared to the conventional EKF, the proposed method can avoid the tedious and time consuming parameter-by-parameter tuning operations. The effectiveness of this method is confirmed experimentally in 128 Gb/s 16 QAM polarization-division-multiplexing（PDM） coherent optical transmission systems. The results illustrate that our proposed AKF has a better tracking accuracy and a faster convergence（about 4 times quicker）compared to a conventional algorithm with optimal process noise covariance.展开更多
基金Supported by the National Natural Science Foundation Committee of China(61503259)China Postdoctoral Science Foundation Funded Project(2017M611261)+1 种基金Chinese Scholarship Council(201608210107)Hanyu Plan of Shenyang Jianzhu University(XKHY2-64)
文摘Building energy consumption accounts for nearly 40% of global energy consumption, HVAC （Heating, Ventilating, and Air Conditioning） systems are the major building energy consumers, and as one type of HVAC systems, the heat pump air conditioning system, which is more energy-efficient compared to the traditional air conditioning system, is being more widely used to save energy. However, in northern China, extreme climatic conditions increase the cooling and heating load of the heat pump air conditioning system and accelerate the aging of the equipment, and the sensor may detect drifted parameters owing to climate change. This non-linear drifted parameter increases the false alarm rate of the fault detection and the need for unnecessary troubleshooting. In order to overcome the impact of the device aging and the drifted parameter, a Kalman filter and SPC （statistical process control） fault detection method are introduced in this paper. In this method, the model parameter and its standard variance can he estimated by Kalman filter based on the gray model and the real-time data of the air conditioning system. Further, by using SPC to construct the dynamic control limits, false alarm rate is reduced. And this paper mainly focuses on the cold machine failure in the component failure and its soft fault detection. This approach has been tested on a simulation model of the ＂Sino-German Energy Conservation Demonstration Center＂ building heat pump air-conditioning system in Shenyang, China, and the results show that the Kalman filter and SPC fault detection method is simple and highly efficient with a low false alarm rate, and it can deal with the difficulties caused by the extreme environment and the non-linear influence of the parameters, and what＇s more, it provides a good foundation for dynamic fault diagnosis and fault prediction analysis.
基金supported by the National Natural Science Foundation of China(NSFC)(Nos.61335005,61325023,and 61401378)
文摘A flexible polarization demultiplexing method based on an adaptive Kalman filter（AKF） is proposed in which the process noise covariance has been estimated adaptively. The proposed method may significantly improve the adaptive capability of an extended Kalman filter（EKF） by adaptively estimating the unknown process noise covariance. Compared to the conventional EKF, the proposed method can avoid the tedious and time consuming parameter-by-parameter tuning operations. The effectiveness of this method is confirmed experimentally in 128 Gb/s 16 QAM polarization-division-multiplexing（PDM） coherent optical transmission systems. The results illustrate that our proposed AKF has a better tracking accuracy and a faster convergence（about 4 times quicker）compared to a conventional algorithm with optimal process noise covariance.
