Anomaly Detection in Rails Using Dimensionality Reduction

Mitsui, S.; Sasaki, Toshihiko; Shinya, Masayoshi; Arai, Y.; Nishimura, Ryutaro

doi:10.2355/isijinternational.isijint-2022-279

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…Residual stress in rails was also determined from X-ray measurements by applying a dimensionality reduction technique to X-ray data characteristic of normal rail regions, followed by multivariate statistical analysis based on the Gaussian distribution; finally, the presence of stress was identified using anomaly detection [113]. After performing dimensionality reduction on the original data using either PCA, kernel PCA, or an autoencoder, each datapoint was given an anomaly score corresponding to the local amount of damage.…”

Section: Microstructural Characterizationmentioning

confidence: 99%

X-ray Diffraction Data Analysis by Machine Learning Methods—A Review

Surdu,

Győrgy

2023

Applied Sciences

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X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has increased significantly over the last decade. This review presents a survey of the scientific literature on applications of ML to XRD data analysis. Publications suitable for inclusion in this review were identified using the “machine learning X-ray diffraction” search term, keeping only English-language publications in which ML was employed to analyze XRD data specifically. The selected publications covered a wide range of applications, including XRD classification and phase identification, lattice and quantitative phase analyses, and detection of defects and substituents, as well as microstructural material characterization. Current trends in the field suggest that future efforts pertaining to the application of ML techniques to XRD data analysis will address shortcomings of ML approaches related to data quality and availability, interpretability of the results and model generalizability and robustness. Additionally, future research will likely incorporate more domain knowledge and physical constraints, integrate with quantum physical methods, and apply techniques like real-time data analysis and high-throughput screening to accelerate the discovery of tailored novel materials.

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Section: Microstructural Characterizationmentioning

confidence: 99%