Optimal sensor placement of triaxial accelerometers for modal expansion

Nieminen, Vesa; Sopanen, Jussi

doi:10.1016/j.ymssp.2022.109581

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…These challenges were recently addressed by Nieminen et al. [ 148 ], who proposed a two-phase OSP method that can be applied to commonly used triaxial accelerometers. The proposed method used the minimum variance criterion to estimate structural responses.…”

Section: Optimal Sensor Placementmentioning

confidence: 99%

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

Hassani

Dackermann

2023

Sensors

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In recent decades, structural health monitoring (SHM) has gained increased importance for ensuring the sustainability and serviceability of large and complex structures. To design an SHM system that delivers optimal monitoring outcomes, engineers must make decisions on numerous system specifications, including the sensor types, numbers, and placements, as well as data transfer, storage, and data analysis techniques. Optimization algorithms are employed to optimize the system settings, such as the sensor configuration, that significantly impact the quality and information density of the captured data and, hence, the system performance. Optimal sensor placement (OSP) is defined as the placement of sensors that results in the least amount of monitoring cost while meeting predefined performance requirements. An optimization algorithm generally finds the “best available” values of an objective function, given a specific input (or domain). Various optimization algorithms, from random search to heuristic algorithms, have been developed by researchers for different SHM purposes, including OSP. This paper comprehensively reviews the most recent optimization algorithms for SHM and OSP. The article focuses on the following: (I) the definition of SHM and all its components, including sensor systems and damage detection methods, (II) the problem formulation of OSP and all current methods, (III) the introduction of optimization algorithms and their types, and (IV) how various existing optimization methodologies can be applied to SHM systems and OSP methods. Our comprehensive comparative review revealed that applying optimization algorithms in SHM systems, including their use for OSP, to derive an optimal solution, has become increasingly common and has resulted in the development of sophisticated methods tailored to SHM. This article also demonstrates that these sophisticated methods, using artificial intelligence (AI), are highly accurate and fast at solving complex problems.

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Section: Optimal Sensor Placementmentioning

confidence: 99%

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

Hassani

Dackermann

2023

Sensors

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“…Optimal Sensor Placement is also implemented in the monitoring of the motion of structures involved in dynamic processes, chiefly the vibrational response of assemblies undergoing periodic forces such as axial compressor casings (8) , helicopter rotor blades (9) , or carbon fibre laminates (10) . Most of these studies comprised the optimal placement of accelerometers (11) . The aim of these studies was to monitor the behaviour of a structure undergoing different modal vibrations which is important in cyclic processes, such as the operation of a tidal turbine, to avoid catastrophic failure when in action.…”

Section: Structure Motion and Performancementioning

confidence: 99%

Use of digital image correlation and machine learning for the optimal strain placement in a full-scale composite tidal turbine blade

McLoughlin,

Munko,

Valdivia Camacho

et al. 2023

Proceedings of the International Conference on Condition Monito

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One of the challenges testing and health monitoring of large structures represents is getting as much information as possible from a specimen with a limited number of sensors. In this work, a data-driven approach was pursued to decide the optimal location of single-point strain gauges using machine learning algorithms (MLA) and information from Digital Image Correlation (DIC) measurements. The optimal strain gauge placement was computed for a range of sensor numbers and the presence of sensors in the high-gradient regions was identified. Strain maps of almost 40,000 measurements were reconstructed successfully with fewer than twenty measured values using the method employed. However, certain loss of image contrast was identified which is likely to have resulted from the treatment of non-numerical values.

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“…The research field of optimal sensor placement extensively employs various indicators as objective functions, including the Modal Assurance Criterion (MAC) (Worden and Burrows, 2001) and information entropy (Papadimitriou, 2004). The optimal placement of accelerometers is considered a subset of optimal sensor placement, and several related studies have utilized accelerometers as sensors (Aminullah et al, 2020;Mahjoubi et al, 2020;Nieminen and Sopanen, 2023). However, the application of accelerometers in these studies has predominantly focused on mounted structural entities like bridges and towers, emphasizing eigenvalue analysis and finite elements (FE).…”

Section: Introductionmentioning

confidence: 99%

Identification of optimal accelerometer placement on trains for railway switch wear monitoring via multibody simulation

Hu,

Lau,

Dai

et al. 2024

Front. Built Environ.

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Accelerometers play a crucial role in the railway industry, especially in track monitoring. Traditionally, they are placed on the railway tracks or often on bridges to monitor the health and condition of the infrastructure. Recently, there has been an increased focus on using regular trains to monitor the condition of railway infrastructure. Often, the sensors are placed based on certain assumptions without much scientific evidence or support. This paper utilizes the multibody simulation software GENSYS to identify the optimal placement of accelerometers on a passenger train for monitoring railway switch wear. Switch wear profiles were generated systematically and used as input for the simulations, studying acceleration at a total of 93 locations distributed among the wheelsets, bogies, and carbody. Based on both time and frequency domain analyses, optimal sensor locations were identified, generally close to the first bogie or wheelset at the leading carbody. Accelerations generated by the wheelset passing the switch can also be captured in the carbody, but it is important to note that these are several orders lower in magnitude compared to the acceleration on the wheelset. If accelerometers are to be placed in the carbody, correct sensitivity must be chosen, and a high-pass filter should be applied to capture the acceleration signals associated with switch wear. The study confirms that there is a direct correlation between the depth of switch wear and the magnitude of the acceleration. It remains effective even under various curve radii and train speeds.

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Optimal sensor placement of triaxial accelerometers for modal expansion

Cited by 12 publications

References 27 publications

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

Use of digital image correlation and machine learning for the optimal strain placement in a full-scale composite tidal turbine blade

Identification of optimal accelerometer placement on trains for railway switch wear monitoring via multibody simulation

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