Optimized Actuator/Sensor Combinations for Structural Health Monitoring: Simulation and Experimental Validation

Venkat, Ramanan Sridaran; Boller, Christian; Ravi, Nitin Balajee; Chakraborty, Nibir; Kamalakar, G. S.; Ukirde, Kishorkumar C; Mahapatra, D. Roy

doi:10.12783/shm2015/128

Cited by 3 publications

(4 citation statements)

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“…Based on the simulation, the locations with the highest peak to peak locations were identified as suitable locations for sensor placement. Venkat et al 68 proposed the locations with points of highest amplitudes in the differential images. The locations were based on points of constructive interference of the wave interactions in order to have a large SNR.…”

Section: Wave Propagation-based Monitoringmentioning

confidence: 99%

“…The locations were based on points of constructive interference of the wave interactions in order to have a large SNR. Ewald et al 69 use a similar differential image approach to that by Venkat et al, 68 but instead of locating sensors at the highest point, they recommend identification of the area of maximum difference using the blob detection algorithm and the ideal sensor location as the centre of the largest blob. These methods are useful for optimization of placement for the known hotspots in the structure.…”

Section: Shm Techniquesmentioning

confidence: 99%

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Optimization of sensor placement for structural health monitoring: a review

Ostachowicz

Soman

Malinowski

2019

Structural Health Monitoring

253

154

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The deployment cost of the structural health monitoring (SHM) system is the major argument against the more widespread use of the structural health monitoring techniques. Optimization of sensor placement offers an opportunity to reduce the cost of the SHM system without compromising on the quality of the monitoring approach. Several studies in the area of optimization of sensor placement for SHM applications have been undertaken but the approach has been rather application specific. This article is an attempt to present an unbiased state of the art of the work carried out in the area. The article is targeted towards researchers working in the field of structural health monitoring and optimization of sensor placement as well as practising engineers. This article reviews the work in the area of optimization of sensor placement. It first presents the definition of the optimization problem and then describes each step of the optimization. The current state of the art is then classified based on the techniques for which the optimization of sensor placement has been optimized. The article covers vibration-based monitoring, strain monitoring and elastic wave-based monitoring, as in the eyes of the authors these three techniques are most commonly used and accepted in the SHM community. The article later discusses the different optimization algorithms that have been applied in the literature. The article highlights the different pitfalls of the optimization algorithms and the countermeasures different researchers have proposed to overcome the known shortcomings. In the later section, the multi-objective optimization or the problem definition, keeping in mind the structural as well as executional demands, is discussed. A section has also been developed to showcase the use of optimization of sensor placement techniques’ data fusion–based systems.

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Section: Wave Propagation-based Monitoringmentioning

confidence: 99%

Section: Shm Techniquesmentioning

confidence: 99%

Optimization of sensor placement for structural health monitoring: a review

Ostachowicz

Soman

Malinowski

2019

Structural Health Monitoring

253

154

View full text Add to dashboard Cite

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“…This relationship can be visualised in a height profile map. Such maps based on computer-aided investigations of an SHM system were presented in [60], [61].…”

Section: Creation Of Pod Mapsmentioning

confidence: 99%

Feasibility of Model-Assisted Probability of Detection Principles for Structural Health Monitoring Systems Based on Guided Waves for Fiber-Reinforced Composites

Tschöke

Mueller

Memmolo

et al. 2021

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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In many industrial sectors, Structural Health Monitoring (SHM) is considered as an addition to Non-Destructive Testing (NDT) that can reduce maintenance effort during lifetime of a technical facility, structural component or vehicle. A large number of SHM methods is based on ultrasonic waves, whose properties change depending on structural health. However, the wide application of SHM systems is limited due to the lack of suitable methods to assess their reliability. The evaluation of the system performance usually refers to the determination of the Probability of Detection (POD) of a test procedure. Up to now, only few limited methods exist to evaluate the POD of SHM systems, which prevent them from being standardised and widely accepted in industry. The biggest hurdle concerning the POD calculation is the large amount of samples needed. A POD analysis requires data from numerous identical structures with integrated SHM systems. Each structure is then damaged at different locations and with various degrees of severity. All of this is connected to high costs. Therefore, one possible way to tackle this problem is to perform computer-aided investigations. In this work, the POD assessment procedure established in NDT according to the Berens model is adapted to guided wave-based SHM systems. The approach implemented here is based on solely computer-aided investigations. After efficient modelling of wave propagation phenomena across an automotive component made of a carbon fibre-reinforced composite, the POD curves are extracted. Finally, the novel concept of a POD map is introduced to look into the effect of damage position on system reliability.

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“…The work done in this area is largely finite element model based, where different damage scenarios are numerically simulated and used to determine the sensor locations such as the one by Lee and Staszewski [12]. Venkat et al [13] and Ewald et al [14] also present a method for locating the sensors at the maxima of the differential image of the healthy and damaged condition of the structure. These methods are useful for optimization of placement for the known hotspots in the structure.…”

Section: Introductionmentioning

confidence: 99%

A Real-Valued Genetic Algorithm for Optimization of Sensor Placement for Guided Wave-Based Structural Health Monitoring

Soman

Malinowski

2019

Journal of Sensors

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The paper presents a novel implementation of the genetic algorithm (GA) to improve the coverage of the sensor network for damage detection using guided waves. The implementation allows depiction of sensor locations with real values which is closer to the real-life situation. Also, additional features such as proximity checks and node insertions have been implemented in order to improve the convergence of the GA as well as the thoroughness of the search space. For the traditional integer-based implementation, the size of the problem is large but finite. For the real-valued implementation, the problem size can indeed be infinitely large. So added measures have been introduced such as a two-step optimization process for the reduction in size and improved convergence.

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Optimized Actuator/Sensor Combinations for Structural Health Monitoring: Simulation and Experimental Validation

Cited by 3 publications

References 0 publications

Optimization of sensor placement for structural health monitoring: a review

Optimization of sensor placement for structural health monitoring: a review

Feasibility of Model-Assisted Probability of Detection Principles for Structural Health Monitoring Systems Based on Guided Waves for Fiber-Reinforced Composites

A Real-Valued Genetic Algorithm for Optimization of Sensor Placement for Guided Wave-Based Structural Health Monitoring

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