Non‐uniform sampling and super‐resolution method to increase the accuracy of tank gauging radar

Kaveh, Sajjad; Norouzi, Yaser

doi:10.1049/iet-rsn.2016.0436

Cited by 4 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Non-contact measurement [1,2], nondestructive testing [3][4][5][6][7] are the key components of robot automatic inspection technology, especially faced with some dangerous, precious, or tender tested objects. Usually, based on some special sensors designed according to one or several physics theories, some features related to the inspected devices were extracted from the recorded signals, then using these features the computer system undertakes the analysis and diagnose work.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission Based Fault Detection of Substation Power Transformer

Luo

Shi

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Fault detection of Substation Power Transformer by Non-contact measurement is important for the safety of machines, instruments, and human beings. To make non-contact measurement as convenient as possible, it is desirable that efficient algorithms based on AE (acoustic emission) discrimination are developed. This paper presents a system for quick and effective fault detection of substation power transformer, based on AE signals collected by non-contact single microphones. In the experiment, collected data were preprocessed in multiple ways and three machine learning algorithms were designed based on classifiers (Convolutional Neural Network (CNN), support vector machine (SVM), and k-nearest neighbors (KNN) algorithm) trained and tested by a tenfold cross-validation technique. After comparison among the designed classifiers, the results show the two-dimensional principal component analysis (2DPCA) preprocess combined with SVM achieved the best comprehensive effectiveness.

show abstract