Optimal Sensor Placement Algorithm Research Articles

Detection of delamination in laminated composites with limited measurements combining PCA and dynamic QPSO

This paper presents an output only damage diagnostic algorithm based on frequency response functions and the principal components for health monitoring of laminated composite structures. The principal components evaluated from frequency response data, are employed as dynamical invariants to handle the effects of operational/environmental variability on the dynamic response of the structure. Finite element models of a laminated composite beam and plate are used to generate vibration data for healthy and damaged structures. Three numerical examples include a laminated composite beam, cantilever plate made of carbon–epoxy and a laminated composite simply supported plate. Varied levels of delamination of laminated composite plies and matrix cracking at varied locations in the plies are simulated at different spatial locations of the structure. Numerical investigations have been carried out to identify the spatial location of damage using the proposed principal component analysis (PCA) based algorithm. In order to limit the number of sensors on the structure, an optimal sensor placement algorithm based on PCA is employed in the present work and the effectiveness of the proposed algorithm with a limited number of sensors is also investigated. Finally, the inverse problem associated with the detection of delamination and matrix cracking is formulated as an optimization problem and is solved using the newly developed dynamic quantum particle swarm optimization (DQPSO) algorithm. Studies carried out and presented in this paper clearly indicate that the proposed SHM scheme can robustly identify the instant of damage, spatial location, the extent of delamination and matrix cracking even with limited sensor measurements and also with noisy data.

Optimal sensor configuration for flexible structures with multi-dimensional mode shapes

A framework for deciding the optimal sensor configuration is implemented for civil structures with multi-dimensional mode shapes, which enhances the applicability of structural health monitoring for existing structures. Optimal sensor placement (OSP) algorithms are used to determine the best sensor configuration for structures with a priori knowledge of modal information. The signal strength at each node is evaluated by effective independence and modified variance methods. Euclidean norm of signal strength indices associated with each node is used to expand OSP applicability into flexible structures. The number of sensors for each method is determined using the threshold for modal assurance criterion (MAC) between estimated (from a set of observations) and target mode shapes. Kriging is utilized to infer the modal estimates for unobserved locations with a weighted sum of known neighbors. A Kriging model can be expressed as a sum of linear regression and random error which is assumed as the realization of a stochastic process. This study presents the effects of Kriging parameters for the accurate estimation of mode shapes and the minimum number of sensors. The feasible ranges to satisfy MAC criteria are investigated and used to suggest the adequate searching bounds for associated parameters. The finite element model of a tall building is used to demonstrate the application of optimal sensor configuration. The dynamic modes of flexible structure at centroid are appropriately interpreted into the outermost sensor locations when OSP methods are implemented. Kriging is successfully used to interpolate the mode shapes from a set of sensors and to monitor structures associated with multi-dimensional mode shapes.

Optimal sensor placement for health monitoring of high-rise structure using adaptive monkey algorithm

Summary Optimal sensor placement is a challenging task in the design of an effective structural health monitoring system. In this paper, a novel optimal sensor placement algorithm, called adaptive monkey algorithm (AMA), to cope with the sensor placement problem for target location under constraints of the computing efficiency and convergence stability is proposed. The dual-structure coding method, instead of the traditional coding method, is adopted to code the solution. The adaptive operator is designed and implemented in the AMA, which provides an automatic technique for adjusting the climb process and watch–jump process of the monkey algorithm according to the observed performance while the search is ongoing. Two new somersault processes, i.e., reflection somersault process and mutation somersault process, are incorporated in the AMA to strengthen its global search ability. Numerical experiments involving two high-rise structures have been carried out to evaluate the performance of the proposed AMA algorithm. The results demonstrated that the innovations in the AMA make it outperform the other algorithms in most cases in terms of less iterations and generating more stable optimal solutions. This algorithm can also be easily applied to other discrete optimization problems. Copyright © 2014 John Wiley & Sons, Ltd.

Optimal sensor placement in the lightweight steel framing structures using the novel TTFD approach subjected to near-fault earthquakes

Lightweight steel framing (LSF) system has been proposed as an economic system and earthquake resistant. In this study, a LSF structure was modeled using finite element method, which the material properties were defined according to laboratory data. Then, the modal and nonlinear time-history analyses were undertaken using the seismic scaled records of near-fault earthquakes. Moreover, three optimal sensor placement (OSP) methods were utilized, and genetic algorithm was selected to act as the solution of the optimization formulation in the selection of the best sensor placement according to structural dynamic response of LSF system. A novel numerical approach was proposed for OSP, which was called transformed time-history to frequency domain (TTFD) algorithm. The TTFD method uses nonlinear time-history analysis results as an exact seismic response despite the common OSP algorithms utilize the eigenvalue responses. Results show that utilizing an efficient OSP method in structural health monitoring process leads to detecting the critical weak points of sensitive structures, so as to retrofit them.

A nearly optimal sensor placement algorithm for boundary coverage

Locating visual sensors in 2D can be often modeled as an Art Gallery problem. Tasks such as surveillance require observing or “covering” the interior of a polygon with a minimum number of sensors or “guards”. For other tasks, such as inspection and image based rendering, observing the boundaries of the environment is sufficient. As Interior Covering (IC), also Edge Covering (EC) is NP-hard, and no finite algorithm is known for its exact solution. Approximate EC solutions are provided by many heuristic algorithms, but their performances with respect to optimality (minimum number of sensors) is unknown. In this paper, we propose a new EC sensors location technique. The algorithm is incremental, and converges toward the optimal solution. It refines an initial approximation provided by an integer covering algorithm (IEC) where each edge is observed entirely by at least one sensor. A lower bound for the number of sensors, specific of the polygon considered, is used at each step for evaluating the quality of the current solution, and a set of rules are provided for performing a local refinement to reduce the computational burden. The algorithm has been implemented, and tests over hundreds of random polygons show that it supplies solutions very close to and often coincident with the lower bound, and then suboptimal or optimal. In addition, the approximate starting solutions provided by the IEC algorithms are, on the average, close to optimum. The tight lower bound can also be used for testing other EC sensor location algorithms. Running times allow dealing with polygons with up to a few hundreds of edges, which appears adequate for many practical cases. An enhanced version of the algorithm, also taking into account range and incidence constraints, has also been implemented and tested.

Identification of dimensional variation patterns on compliant assemblies

This paper presents a methodology to diagnose sources of dimensional variation for compliant parts from the measurement data of final assemblies. The method is developed for a single-station assembly process. The proposed diagnosis tool is based on applying a predictive variation propagation model to determine the part-to-part and tooling interaction in the assembly system. The variation propagation model allows identifying the impact of different faulty component patterns in the final assembly product. Using the predictive assembly fault patterns and the designated component analysis, the contribution of each fault in the total system variation may be identified. The methodology incorporates an optimal sensor placement algorithm to determine the key measurement points in the assembly. Two case studies were conducted to illustrate that the methodology is capable of identifying part variation patterns from assembly measurement data, even under significant levels of noise. Although the methodology is presented for a single assembly station, it can be extended to a multiple-station assembly scenario using a multistation variation propagation model.