1,721,027 research outputs found
Distributed Fault Diagnosis for Continuous-Time Nonlinear Systems: the Input-Output case
No full textIn this paper, some new results on distributed fault diagnosis of continuous--time nonlinear systems with partial state measurements are proposed. By exploiting an overlapping decomposition framework, the dynamics of a nonlinear uncertain large-scale dynamical system is described as the interconnections of several subsystems. Each subsystem is monitored by a Local Fault Diagnoser: a set of local estimators, based on the nominal local dynamic model and on an adaptive approximation of the interconnection and of the fault function, allows to derive a local fault decision. A consensus-based protocol is used in order to improve the detectability and the isolability of faults affecting variables shared among different subsystems because of the overlapping decomposition. A sufficient condition ensuring the convergence of the estimation errors is derived. Finally, possibly non-conservative time-varying threshold functions guaranteeing no false-positive alarms and theoretical results dealing with detectability and isolability sufficent conditions are presented
Decentralized state estimation for the control of network systems
No full textThe paper proposes a decentralized state estimation method for the control of network systems, where a cooperative objective has to be achieved. The nodes of the network are partitioned into independent nodes, providing the control inputs, and dependent nodes, controlled by local interaction laws. The proposed state estimation algorithm allows the independent nodes to estimate the state of the dependent nodes in a completely decentralized way. To do that, it is necessary for each independent node of the network to estimate the control input components computed by the other independent nodes, without requiring communication among the independent nodes. The decentralized state estimator, including an input estimator, is developed and the convergence properties are studied. Simulation results show the effectiveness of the proposed approach
A Distributed Estimation Method for Sensor Networks Based on Pareto Optimization
No full textA novel distributed estimation method for sensor networks is proposed. The goal is to track a time-varying signal that is jointly measured by a network of sensor nodes despite the presence of noise: each node computes its local estimate as a weighted sum of its own and its neighbors' measurements and estimates and updates its weights to minimize both the variance and the mean of the estimation error by means of a suitable Pareto optimization problem. The estimator does not rely on a central coordination: both parameter optimization and estimation are distributed across the nodes. The performance of the distributed estimator is investigated in terms of estimation bias and estimation error. Moreover, an upper bound of the bias is provided. The effectiveness of the proposed estimator is illustrated via computer simulations and the performances are compared with other distributed schemes previously proposed in the literature. The results show that the estimation quality is comparable to that of one of the best existing distributed estimation algorithms, guaranteeing lower computational cost and time.</p
Distributed Fault Detection using Sensor Networks and Pareto Estimation
No full textIn this paper, a preliminary novel distributed fault detection architecture for dynamic systems using sensor networks and a distributed estimation method based on Pareto optimization is proposed. The goal is to monitor large-scale or distributed systems by using a sensor network where each node acts as a local estimation agent without centralized coordination. Probabilistic detection thresholds related to a given rate of false alarms are derived in several different scenarios as far as the measurement pattern and the nominal dynamics is concerned. Preliminary simulation results show the effectiveness of the proposed fault detection methodology.QC 20131219</p
Trajectory clustering by means of Earth Mover's Distance
No full textWe propose a method for trajectory classification based on a general cluster-based methodology, that can be used both off-line in an unsupervised fashion, both on-line, classifying new trajectories or part of them. We use the Earth Mover’s Distance (EMD) and we adapt it in order to employ it as a tool for trajectory clustering. We propose a novel effective method to identify the clusters’ representatives by means of the p−median location problem. This methodology is able to manage different length and noisy trajectories and takes velocity profiles and stops into account. We discuss the experimental results and we compare our approach with other trajectory clustering methods
Decentralized fault diagnosis for heterogeneous multi-agent systems
Full text linkThe paper proposes a decentralized method for
fault detection and isolation in heterogeneous multi-agents
systems. The agents are partitioned into independent nodes,
providing the control inputs and monitoring the system, and
dependent nodes, controlled by local interaction laws and sub-
ject to faults. The approach uses a decentralized state estimation
algorithm allowing the independent nodes to estimate both the
state of the dependent nodes and the control input components
computed by the other independent nodes, in a completely
decentralized way, without requiring communication among
the independent nodes. Suitable detection and isolation resid-
uals and thresholds are derived. Detectability and isolability
sufficient conditions are provided. Simulation results show the
effectiveness of the proposed approach
Reducing false alarm rates in observer-based distributed fault detection schemes by analyzing moving averages
Full text linkIn this paper, we first analyze the possible limitations of a model-based fault detection method grounded on a partition-based distributed Luenberger observer. The corresponding fault detection test consists of comparing, for each time instant, the output prediction error with a suitable bound, computed analytically in a distributed and scalable way As a result, we highlight the presence of an often restrictive tradeoff between false-alarm and missed-detection rates. To overcome this significant drawback, we resort to a method based on the analysis of moving averages of residuals. Tests on an academic case study show the effectiveness of this approach
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