Identifiability of parametric models
Identifiability of parametric models
Distributed parameter systems: theory and applications
Distributed parameter systems: theory and applications
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Convex Optimization
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
D-optimal design of a monitoring network for parameter estimation of distributed systems
Journal of Global Optimization
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
Survey A review of methods for input/output selection
Automatica (Journal of IFAC)
A Parallel Sensor Scheduling Technique for Fault Detection in Distributed Parameter Systems
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Monitoring distributed parameter systems based on expert systems and sensor networks
NNECFSIC'12 Proceedings of the 12th WSEAS international conference on Neural networks, fuzzy systems, evolutionary computing & automation
Sensor network design for the estimation of spatially distributed processes
International Journal of Applied Mathematics and Computer Science
Fault tolerance in networked control systems under intermittent observations
International Journal of Applied Mathematics and Computer Science
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The problem of fault detection in distributed parameter systems (DPSs) is formulated as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A computational scheme is provided for the design of a network of observation locations in a spatial domain that are supposed to be used while detecting changes in the underlying parameters of a distributed parameter system. The setting considered relates to a situation where from among a finite set of potential sensor locations only a subset can be selected because of the cost constraints. As a suitable performance measure, the Ds-optimality criterion defined on the Fisher information matrix for the estimated parameters is applied. Then, the solution of a resulting combinatorial problem is determined based on the branch-and-bound method. As its essential part, a relaxed problem is discussed in which the sensor locations are given a priori and the aim is to determine the associated weights, which quantify the contributions of individual gauged sites. The concavity and differentiability properties of the criterion are established and a gradient projection algorithm is proposed to perform the search for the optimal solution. The delineated approach is illustrated by a numerical example on a sensor network design for a two-dimensional convective diffusion process.