Abstract

A novel fault tree analysis (FTA) technique based on the Takagi and Sugeno (T-S) model is proposed in this paper. In the proposed technique, referred to as the TS-FTA, the events in the conventional FTA can be expressed in terms of fuzzy possibilities, and the gates that represent the relations among the top event and the primary events are replaced by T-S fuzzy gates derived from the T-S model. The magnitudes of the faults in the system are expressed in term of fuzzy variables. Since the proposed TS-FTA is derived from fuzzy logic and the T-S model, it can readily handle fuzzy information and uncertainties in the relationships among events. Therefore, the TS-FTA is suitable for systems where exact information on the fault probabilities of the components and the failure mechanisms are not available. The proposed TS-FTA is applied to analyze the reliability of an Inertial Navigation System and Global Position System (INS/GPS) integrated navigation system.

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A multiple fault detection and identification method based on fully decoupled parity equations for dynamic systems with known linear and unknown nonlinear terms is presented. The fully decoupled parity equation vectors is derived and it is shown that the residuals generated from it are decoupled from other faults and the unknown nonlinear term and are sensitive only to specific actuator or sensor faults. The condition for the existence of the equation is also given. From the residuals generated from the fully decoupled parity equation, the faults are estimated using the recursive least-squares method. The performance of the proposed method is illustrated by applying it to detect, isolate, and identify faults in a simulated dc motor.

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Reconfigurable intelligent surface (RIS) as an emerging cost-effective technology can enhance the spectrum-and energy-efficiency of wireless networks. In this paper, we consider an RIS-aided green edge inference system, where the inference tasks generated from resource-limited mobile devices (MDs) are uploaded to and cooperatively performed at multiple resourceenhanced base stations (BSs). Taking into account both the computation and uplink/downlink transmit power consumption, we formulate an overall network power consumption minimization problem, which calls for the joint design of the set of tasks performed by each BS, transmit and receive beamforming vectors of the BSs, transmit power of the MDs, and uplink/downlink phase-shift matrices at the RIS. Such a problem is a mixed combinatorial optimization problem with nonconvex constraints and is highly intractable. To address the challenge of the combinatorial objective, a group sparse reformulation is proposed by exploiting the group sparsity structure of the beamforming vectors, while a block-structured optimization (BSO) approach is proposed to decouple the optimization variables. Finally, we propose a BSO with mixed 1,2-norm and difference-of-convex-functions (DC) based three-stage framework to solve the problem, where the mixed 1,2-norm is adopted to induce the group sparsity of beamforming vectors and DC is adopted to effectively handle the nonconvex rank-one constraint after matrix lifting. Numerical results demonstrate the supreme gain of deploying an RIS and confirm the effectiveness of the proposed algorithm over the baseline algorithms.Index Terms-Reconfigurable intelligent surface, joint uplink and downlink, green edge inference, block-structured optimization, mixed 1,2-norm,

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