Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning pr...Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.展开更多
This paper presents a multi-mode control scheme for a soft-switched flyback converter to achieve high efficiency and excellent load regulation over the entire load range. At heavy load, critical conduction mode with v...This paper presents a multi-mode control scheme for a soft-switched flyback converter to achieve high efficiency and excellent load regulation over the entire load range. At heavy load, critical conduction mode with valley switching (CCMVS) is employed to realize soft switching so as to reduce turn-on loss of power switch as well as conducted electromagnetic interference (EMI). At light load, the converter operates in discontinuous conduction mode (DCM) with valley switching and adaptive off-time control (AOT) to limit the switching frequency range and maintain load regulation. At extremely light load or in standby mode, burst mode operation is adopted to provide low power consumption through reducing both switching frequency and static power dissipation of the controller. The multi-mode control is implemented by an oscillator whose pulse duration is adjusted by output feedback. An accurate valley switching control circuit guarantees the minimum turn-on voltage drop of power switch. The pro-totype of the controller IC was fabricated in a 1.5-μm BiCMOS process and applied to a 310 V/20 V, 90 W flyback DC/DC converter circuitry. Experimental results showed that all expected functions were realized successfully. The flyback converter achieved a high efficiency of over 80% from full load down to 2.5 W, with the maximum reaching 88.8%, while the total power consumption in standby mode was about 300 mW.展开更多
In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detec...In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.展开更多
基金Supported by the National Natural Science Foundation of China(61374137,61490701,61174119)the State Key Laboratory of Integrated Automation of Process Industry Technology and Research Center of National Metallurgical Automation Fundamental Research Funds(2013ZCX02-03)
文摘Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.
基金the National Natural Science Foundation of China (No. 90707002)the Natural Science Foundation of Zheji-ang Province, China (No. Z104441)
文摘This paper presents a multi-mode control scheme for a soft-switched flyback converter to achieve high efficiency and excellent load regulation over the entire load range. At heavy load, critical conduction mode with valley switching (CCMVS) is employed to realize soft switching so as to reduce turn-on loss of power switch as well as conducted electromagnetic interference (EMI). At light load, the converter operates in discontinuous conduction mode (DCM) with valley switching and adaptive off-time control (AOT) to limit the switching frequency range and maintain load regulation. At extremely light load or in standby mode, burst mode operation is adopted to provide low power consumption through reducing both switching frequency and static power dissipation of the controller. The multi-mode control is implemented by an oscillator whose pulse duration is adjusted by output feedback. An accurate valley switching control circuit guarantees the minimum turn-on voltage drop of power switch. The pro-totype of the controller IC was fabricated in a 1.5-μm BiCMOS process and applied to a 310 V/20 V, 90 W flyback DC/DC converter circuitry. Experimental results showed that all expected functions were realized successfully. The flyback converter achieved a high efficiency of over 80% from full load down to 2.5 W, with the maximum reaching 88.8%, while the total power consumption in standby mode was about 300 mW.
文摘In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.
