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Modeling of reliability and performance assessment of a dissimilar redundancy actuation system with failure monitoring

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Chinese Journal of Aeronautics 2016年第3期29卷 799-813页

作者： Wang Shaoping Cui Xiaoyu Shi Jian Mileta M.Tomovic Jiao Zongxia School of Automation Science and Electrical Engineering Beihang University College of Engineering and Technology Old Dominion University

Actuation system is a vital system in an aircraft, providing the force necessary to move flight control surfaces. The system has a significant influence on the overall aircraft performance and its safety. In order to further increase already high reliability and safety, Airbus has imple- mented a dissimilar redundancy actuation system （DRAS） in its aircraft. The DRAS consists of a hydraulic actuation system （HAS） and an electro-hydrostatic actuation system （EHAS）, in which the HAS utilizes a hydraulic source （HS） to move the control surface and the EHAS utilizes an elec- trical supply （ES） to provide the motion force. This paper focuses on the performance degradation processes and fault monitoring strategies of the DRAS, establishes its reliability model based on the generalized stochastic Petri nets （GSPN）, and carries out a reliability assessment considering the fault monitoring coverage rate and the false alarm rate. The results indicate that the proposed reli- ability model of the DRAS, considering the fault and redundancy degradation process and identify monitoring, can express its fault logical relation potential safety hazards.

关键词： Dissimilar redundancy actu-ation system Electro-hydraulic actuationsystem fault monitoring Generalized stochastic Petrinets Performance degradation

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Batch Process monitoring Based on Multi-stage Fourth Order Moment Stacked Autoencoder

Batch Process Monitoring Based on Multi-stage Fourth Order M...

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第32届中国控制与决策会议

作者： Jin Chen Wang Pu Wang Kai Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

fault monitoring can find out-of-control conditions of equipment operation in a timely manner, which is essential for eliminating faults and for stable operation of industrial systems in batch processes. Many conventional data-driven fault detection methods focus less on the non-Gaussian and Multi-stage characteristics of batch process data, which may result in degradation of monitoring performance. In this paper, a Multi-stage Fourth Order Moment Staked Autoencoder（M-FOM-SAE） is designed to solve the above problems. The proposed method firstly automatically determines the number of clusters and divides the batch process into multiple stages. After that, the FOM-SAE model is established in each sub-stage, which can not only effectively learn the nonlinear features of process data, but also extract the non-Gaussian information. The proposed strategy is applied to real-world industrial processes. Experimental results indicate that it can better capture the non-Gaussian and Multi-stage characteristics of process data, and improve the ability to monitor abnormalities.

关键词： Batch process fault monitoring non-Gaussian Multi-stage Stacked Autoencoder

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