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Wang T.,Shanghai Maritime University | Wang T.,Naval Academy Research Institute of France | Wu H.,Shanghai Maritime University | Liu P.,Shanghai Maritime University | And 3 more authors.
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | Year: 2014

The traditional multivariate statistical methods could not detect the fault effectively under the non-steady condition of complex system. For these problems above, the concept of the dynamic limit and the same peak-valley are given in this paper. And, the paper proves that the T2 statistics of the multivariable parameters is periodic if the system is under the periodic non-steady condition. Then, this paper proposes a model of real-time fault detection under the periodic non-steady condition and gives the real time and the feasibility analysis of the model. At last, the model is applied to the periodic non-steady conditions in real time. ©, 2014, Chinese Machine Press. All right reserved. Source


Wang T.,Shanghai Maritime University | Huang X.,Chinese University of Hong Kong | Claramunt C.,Naval Academy Research Institute of France | Zhang Q.,Shanghai Maritime University | And 3 more authors.
International Journal of Engineering Education | Year: 2015

With the continuous development of information and telecommunication technologies, intelligent control has been penetrating into every aspect of human's life. Automobile manufacturing is no exception. In particular, teaching laboratories that combine intelligent control with automobile manufacturing have recently attracted teachers' attention from various universities and colleges. The intelligent car lab presented in this paper is developed on top of a competition developed by Freescale, a semiconductor company, together with the Chinese Higher Education Automation Professional Education Committee in China. This competition is held in several Asian countries for the sake of cultivating students' practical abilities. The paper introduces the whole learning environment process, preparation made by teachers and students from Shanghai Maritime University, and the setup of a concept of adaptive intelligent car lab. We introduce the project components, technical developments achieved by the students and discuss the benefits of the whole experience. We show that throughout this proactive learning environment, students learnt a large amount of technological and design knowledge and enhanced their hands-on technical abilities, as well as learning new group-project capabilities, thus providing a solid foundation for their professional future. © 2015 TEMPUS Publications. Source


Wang T.-Z.,Shanghai Maritime University | Wang T.-Z.,Naval Academy Research Institute of France | Xu M.,Shanghai Maritime University | Tang T.-H.,Shanghai Maritime University | Claramunt C.,Naval Academy Research Institute of France
WSEAS Transactions on Circuits and Systems | Year: 2013

The multivariate statistical methods are commonly used to fault detection through a straight limit line given by the HotellingT2. However, the traditional straight limit line is difficult to detect the fault effectivelyunder the non-steady conditions, and the rate of false alarmand missing alarm is high. For these problems above, a fault detection method based on dynamic peak-valley limit is proposed in this paper. The proposed method introduces relative principal component analysis (RPCA) to carry out data dimension reduction, extractprincipal component (PCs) and calculate T 2 statistics, then adopts moving least squares(MLS) to preprocessT2 statistics to obtain the fitting curve which is called peak-valley curve, and finally connects peak and valley points in the curve to construct another control limit, by introducing a weight combined with the traditional straight limit line to construct the dynamic peak-valley limit. At the end, it is applied to wind power generation system, and the results could verify the effectiveness of the method. Source


Wang T.,Shanghai Maritime University | Wang T.,Naval Academy Research Institute of France | Liu Y.,Shanghai Maritime University | Tang T.,Shanghai Maritime University | Chen Y.,Shanghai Maritime University
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | Year: 2013

In traditional principal component analysis(PCA), because of the neglect of the influence of dimension standardization, it was difficult to extract principal components(PCs) effectively. The fault detection method based on relative principal component analysis(RPCA), its control limit is related to the number of PCs and confidence. For these problems, a dynamic data window method based on RPCA is proposed in this paper. The proposed method combined the traditional control limit and dynamic data window by introducing a weight. Finally, it is applied to wind power generation system, can detect failures effectively and reduce the rate of false alarm. Source


Wang T.,Shanghai Maritime University | Wang T.,Naval Academy Research Institute of France | Tang T.,Shanghai Maritime University | Zhang S.,Shanghai Maritime University | Claramunt C.,Naval Academy Research Institute of France
Gaojishu Tongxin/Chinese High Technology Letters | Year: 2012

The standard particle filter (SPF) algorithm's problem of low targer-tracking performance due to the serious granule degeneration that occurs when the state model deviates the target's motion state is paid attention, and aiming at this, a particle filter algorithm based on the gray forecast theory, called the PFGF algorithm is presented with the detailed description of it. When the condition model established in advance is no longer suitable for the goal's proper motion condition, this new algorithm is with the good estimate performance. It reduces the dependence on the beforehand target condition model. The Monte-Carlo simulation results show that the new algorithm increases the tracking accuracy without increasing the computation complexity compared with the SPF algorithm. It can overcome the phenomenon of granule degeneration effectively. Source

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