Gas temperature measurement by atomic line broadening using the LIBS technique

Gao, Ercong; Wei, Renmin; Dayuan, Zhang; Zhu, Zhiwei; Gao, Qiang; Li, Bo

doi:10.1039/d3ja00044c

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Gao et al 79 determined T gas using a new optical temperature measurement technique for gas flow fields using ns-LIBS atomic spectral line broadening. The authors cite several advantages for this technique which are, in summary: it is an experimentally simple, single-pulse technique which does not require complex data processing; it can be performed simultaneously with other LIBS measurements, so can thus be used to improve the reliability of other LIBS applications; it has a strong signal so is more robust; it is independent of the laser pulse energy, pulse width, and focal spot size when using laser energy above the breakdown threshold and it is relatively sensitive.…”

Section: Instrumentation Fundamentals and Chemometricsmentioning

confidence: 99%

Atomic spectrometry update: review of advances in atomic spectrometry and related techniques

Evans,

Pisonero,

Smith

et al. 2024

J. Anal. At. Spectrom.

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Section: Instrumentation Fundamentals and Chemometricsmentioning

confidence: 99%

Atomic spectrometry update: review of advances in atomic spectrometry and related techniques

Evans,

Pisonero,

Smith

et al. 2024

J. Anal. At. Spectrom.

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show abstract

Detection of defects in composite insulators based on laser‐induced plasma combined with machine learning

Xu,

Che

2024

Micro & Optical Tech Letters

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Due to Prolonged operation and external environmental factors, composite insulators may develop various defects, which can potentially lead to serious accidents in power systems. Detecting and analyzing these defects is critically important. In this study, we combine machine learning with laser‐induced breakdown spectroscopy (LIBS) to identify defects in composite insulators and obtain spectral data from both defective and nondefective samples. The Uniform Manifold Approximation and Projection algorithm was employed to reduce the dimensionality of the data, thereby enhancing detection accuracy and efficiency. Decision tree, random forest (RF), K‐nearest neighbor, and support vector machine algorithms were used to classify the dimensionality‐reduced data. The results indicate that the RF algorithm achieved the best classification performance, with accuracies of 100% and 95.46% for the training and test sets, respectively. Furthermore, the precision, recall, and F1 scores, which reflect model performance, also showed superior results with the RF algorithm. These findings suggest that the combination of LIBS technology and machine learning can rapidly and accurately detect and analyze defects in composite insulators, offering new insights and methods for defect detection in composite insulators.

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One-dimensional temperature measurement of gases based on femtosecond laser-extended electrode discharge spectroscopy (FEEDS)

Feng,

Han,

Gao

et al. 2024

Optics and Lasers in Engineering

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Gas temperature measurement by atomic line broadening using the LIBS technique

Abstract: Accurate temperature measurement of the gas flow field is of great significance in theoretical research and engineering applications. Here, we found that when the laser-induced breakdown spectroscopy (LIBS) technique is...

Cited by 3 publications

References 27 publications

Atomic spectrometry update: review of advances in atomic spectrometry and related techniques

Atomic spectrometry update: review of advances in atomic spectrometry and related techniques

Detection of defects in composite insulators based on laser‐induced plasma combined with machine learning

One-dimensional temperature measurement of gases based on femtosecond laser-extended electrode discharge spectroscopy (FEEDS)

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