Obtaining High Accuracy Vibration Measurements With Low-Cost Sensors Using Bayesian Virtual Sensing

Kullaa, Jyrki

doi:10.7712/120217.5348.16778

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…In [11], the OSP algorithm suggested is an expansion error minimization FSSP (FSSP-EEM), in which the mean of the estimation error variance is evaluated after placing each sensor in candidate locations, which are weighted according to the signal-to-noise ratio. In [12], Kullaa investigated and compared the performance of three OSP methods for VS -the well-known Effective Independence (EFI) [13], FSSP, and Backward Sequential Sensor Placement (BSSP), concluding that the BSSP outperforms the other two, achieving the same accuracy with fewer sensors.…”

Section: Introductionmentioning

confidence: 99%

An experimental study on the performance of virtual sensing using optimal and regular physical sensors placement

Bourdalos,

Zisopoulos,

Tcherniak

et al. 2024

J. Phys.: Conf. Ser.

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Vibration analysis is highly beneficial in a variety of engineering areas. However, in many real-world applications, vibration data acquisition may be challenging due to the accessibility of the desired sensor locations. It can be also costly if many measurement points are required. Consequently, a few vibration estimation methods have been proposed, which are referred to as “virtual sensing”. Virtual sensing claims to be able to replace a physical sensor with a virtual one, whose signal should closely resemble the signal from the physical sensor if it was placed at the same location. The signal from such a virtual sensor is estimated based on a numerical model of the structure under test and data from a number of physical sensors. In this study, the well-known modal expansion and decomposition-based virtual sensing method is examined, and its sensitivity to the amount and location of physical sensors is explored. Two sensor placement scenarios are considered: (i) the most common scenario where the physical sensors are placed in the nodes of a regular mesh, and (ii) where the sensors configuration is generated by the optimal sensors placement (OSP) algorithm. The experimental examination is performed on a simple test structure (rectangular aluminum plate) using time and frequency domain performance indicators for three excitation profiles (pseudo-random, burst pseudorandom, and sinusoidal). The results demonstrate that the use of OSP significantly improves the performance of virtual sensing.

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Section: Introductionmentioning

confidence: 99%